CS145: INTRODUCTION TO DATA MINING
|
|
- Brendan Gilbert
- 5 years ago
- Views:
Transcription
1 CS145: INTRODUCTION TO DATA MINING 3: Vector Data: Logistic Regression Instructor: Yizhou Sun October 9, 2017
2 Methods to Learn Vector Data Set Data Sequence Data Text Data Classification Clustering Logistic Regression; Decision Tree; KNN SVM; NN K-means; hierarchical clustering; DBSCAN; Mixture Models Naïve Bayes for Text PLSA Prediction Frequent Pattern Mining Similarity Search Linear Regression GLM* Apriori; FP growth GSP; PrefixSpan DTW 2
3 Vector Data: Logistic Regression Classification: Basic Concepts Logistic Regression Model Generalized Linear Model* Summary 3
4 Supervised vs. Unsupervised Learning Supervised learning (classification) Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations New data is classified based on the training set Unsupervised learning (clustering) The class labels of training data is unknown Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data 4
5 Prediction Problems: Classification vs. Numeric Prediction Classification predicts categorical class labels classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data Numeric Prediction models continuous-valued functions, i.e., predicts unknown or missing values Typical applications Credit/loan approval: Medical diagnosis: if a tumor is cancerous or benign Fraud detection: if a transaction is fraudulent Web page categorization: which category it is 5
6 Classification A Two-Step Process (1) Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute For data point i: < xx ii, yy ii > Features: xx ii ; class label: yy ii The model is represented as classification rules, decision trees, or mathematical formulae Also called classifier The set of tuples used for model construction is training set 6
7 Classification A Two-Step Process (2) Model usage: for classifying future or unknown objects Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Test set is independent of training set (otherwise overfitting) Accuracy rate is the percentage of test set samples that are correctly classified by the model Most used for binary classes If the accuracy is acceptable, use the model to classify new data Note: If the test set is used to select models, it is called validation (test) set 7
8 Process (1): Model Construction Training Data Classification Algorithms NAME RANK YEARS TENURED Mike Assistant Prof 3 no Mary Assistant Prof 7 yes Bill Professor 2 yes Jim Associate Prof 7 yes Dave Assistant Prof 6 no Anne Associate Prof 3 no Classifier (Model) IF rank = professor OR years > 6 THEN tenured = yes 8
9 Process (2): Using the Model in Prediction Classifier Testing Data Unseen Data NAME RANK YEARS TENURED Tom Assistant Prof 2 no Merlisa Associate Prof 7 no George Professor 5 yes Joseph Assistant Prof 7 yes (Jeff, Professor, 4) Tenured? 9
10 Vector Data: Logistic Regression Classification: Basic Concepts Logistic Regression Model Generalized Linear Model* Summary 10
11 Linear Regression VS. Logistic Regression Linear Regression (prediction) Y: cccccccccccccccccccc vvvvvvvvvv, + Y = xx TT ββ = ββ 0 + xx 1 ββ 1 + xx 2 ββ xx pp ββ pp Y xx, ββ~nn(xx TT ββ, σσ 2 ) Logistic Regression (classification) Y: dddddddddddddddd vvvvvvvvvv ffffffff mm cccccccccccccc pp YY = CC jj [0,1] aaaaaa jj pp YY = CC jj = 1 11
12 Logistic Function Logistic Function / sigmoid function: σσ xx = 1 1+ee xx NNNNNNNN: σσ xx = σσ(xx)(1 σσ(xx)) 12
13 Modeling Probabilities of Two Classes PP YY = 1 XX, ββ = σσ XX TT ββ = 1 1+exp{ XX TT ββ} = exp{xxtt ββ} 1+exp{XX TT ββ} PP YY = 0 XX, ββ = 1 σσ XX TT ββ = exp{ XXTT ββ} 1+exp{ XX TT ββ} = 1 1+exp{XX TT ββ} ββ = ββ 0 ββ 1 ββ pp In other words YY X, ββ~bbeeeeeeeeeeeeeeee(σσ XX TT ββ ) 13
14 The 1-d Situation PP YY = 1 xx, ββ 0, ββ 1 = σσ ββ 1 xx + ββ , =0, =1 0 1 =0, =2 0 1 ) 1 =5, = Prob(Y=1 x, x 14
15 Example Q: What is ββ 0 here? 15
16 MLE estimation Parameter Estimation Given a dataset DD, wwwwwww nn dddddddd pppppppppppp For a single data object with attributes xx ii, class label yy ii Let pp ii = pp YY = 1 xx ii, ββ, ttttt pppppppp. oooo ii iiii cccccccccc 1 The probability of observing yy ii would be If yy ii = 1, ttttttt pp ii If yy ii = 0, ttttttt 1 pp ii yy Combing the two cases: pp ii ii 1 1 yy ppii ii LL = ii pp ii yy ii 1 ppii 1 yy ii = ii exp XX TT ββ 1+exp XX TT ββ yy ii 1 1+exp XX TT ββ 1 yy ii 16
17 Optimization Equivalent to maximize log likelihood LL = ii yy ii xx ii TT ββ log 1 + exp xx ii TT ββ Gradient ascent update: ββ nnnnnn = ββ oooooo + ηη (ββ) Newton-Raphson update Step size where derivatives are evaluated at ββ old 17
18 First Derivative pp(xx ii ; ββ) j = 0, 1,, p 18
19 Second Derivative It is a (p+1) by (p+1) matrix, Hessian Matrix, with jth row and nth column as 19
20 What about Multiclass Classification? It is easy to handle under logistic regression, say M classes PP YY = jj XX = 1,, MM 1 PP YY = MM XX = 1+ mm=1 exp{xx TT ββ jj } MM 1 exp{xx TT ββ mm }, for j = 1 MM 1 exp{xx TT ββ mm } 1+ mm=1 20
21 Vector Data: Logistic Regression Classification: Basic Concepts Logistic Regression Model Generalized Linear Model* Summary 21
22 Recall Linear Regression and Logistic Regression Linear Regression y xx, ββ~nn(xx TT ββ, σσ 2 ) Logistic Regression y xx, ββ~bbbbbbbbbbbbbbbbbb(σσ xx TT ββ ) How about other distributions? Yes, as long as they belong to exponential family 22
23 Canonical Form Exponential Family pp yy; ηη = bb yy exp ηη TT TT yy aa ηη ηη: natural parameter TT yy : sufficient statistic aa ηη : log partition function for normalization bb yy : function that only dependent on yy 23
24 Examples of Exponential Family Many: pp yy; ηη = bb yy exp ηη TT TT yy aa ηη Gaussian, Bernoulli, Poisson, beta, Dirichlet, categorical, For Gaussian (not interested in σσ) For Bernoulli ηη 24
25 Recipe of GLMs Determines a distribution for y E.g., Gaussian, Bernoulli, Poisson Form the linear predictor for ηη ηη = xx TT ββ Determines a link function: μμ = gg 1 (ηη) Connects the linear predictor to the mean of the distribution E.g., μμ = ηη for Gaussian, μμ = σσ ηη for Bernoulli, μμ = eeeeee ηη for Poisson 25
26 Vector Data: Logistic Regression Classification: Basic Concepts Logistic Regression Model Generalized Linear Model* Summary 26
27 Summary What is classification Supervised learning vs. unsupervised learning, classification vs. prediction Logistic regression Sigmoid function, multiclass classification Generalized linear model* Exponential family, link function 27
CS249: ADVANCED DATA MINING
CS249: ADVANCED DATA MINING Linear Regression, Logistic Regression, and GLMs Instructor: Yizhou Sun yzsun@cs.ucla.edu April 24, 2017 About WWW2017 Conference 2 Turing Award Winner Sir Tim Berners-Lee 3
More informationSpecial Topics: Data Science
Special Topics: Data Science L Linear Methods for Prediction Dr. Vidhyasaharan Sethu School of Electrical Engineering & Telecommunications University of New South Wales Sydney, Australia V. Sethu 1 Topics
More informationMixture Models & EM. Nicholas Ruozzi University of Texas at Dallas. based on the slides of Vibhav Gogate
Mixture Models & EM Nicholas Ruozzi University of Texas at Dallas based on the slides of Vibhav Gogate Previously We looked at -means and hierarchical clustering as mechanisms for unsupervised learning
More informationLogistic Regression. Hongning Wang
Logistic Regression Hongning Wang CS@UVa Today s lecture Logistic regression model A discriminative classification model Two different perspectives to derive the model Parameter estimation CS@UVa CS 6501:
More informationBayesian Methods: Naïve Bayes
Bayesian Methods: Naïve Bayes Nicholas Ruozzi University of Texas at Dallas based on the slides of Vibhav Gogate Last Time Parameter learning Learning the parameter of a simple coin flipping model Prior
More informationDecision Trees. Nicholas Ruozzi University of Texas at Dallas. Based on the slides of Vibhav Gogate and David Sontag
Decision Trees Nicholas Ruozzi University of Texas at Dallas Based on the slides of Vibhav Gogate and David Sontag Announcements Course TA: Hao Xiong Office hours: Friday 2pm-4pm in ECSS2.104A1 First homework
More informationknn & Naïve Bayes Hongning Wang
knn & Naïve Bayes Hongning Wang CS@UVa Today s lecture Instance-based classifiers k nearest neighbors Non-parametric learning algorithm Model-based classifiers Naïve Bayes classifier A generative model
More informationEvaluating and Classifying NBA Free Agents
Evaluating and Classifying NBA Free Agents Shanwei Yan In this project, I applied machine learning techniques to perform multiclass classification on free agents by using game statistics, which is useful
More informationLecture 5. Optimisation. Regularisation
Lecture 5. Optimisation. Regularisation COMP90051 Statistical Machine Learning Semester 2, 2017 Lecturer: Andrey Kan Copyright: University of Melbourne Iterative optimisation Loss functions Coordinate
More informationECO 745: Theory of International Economics. Jack Rossbach Fall Lecture 6
ECO 745: Theory of International Economics Jack Rossbach Fall 2015 - Lecture 6 Review We ve covered several models of trade, but the empirics have been mixed Difficulties identifying goods with a technological
More informationCourse 495: Advanced Statistical Machine Learning/Pattern Recognition
Course 495: Advanced Statistical Machine Learning/Pattern Recognition Lectures: Stefanos Zafeiriou Goal (Lectures): To present modern statistical machine learning/pattern recognition algorithms. The course
More informationISyE 6414 Regression Analysis
ISyE 6414 Regression Analysis Lecture 2: More Simple linear Regression: R-squared (coefficient of variation/determination) Correlation analysis: Pearson s correlation Spearman s rank correlation Variable
More informationPREDICTING the outcomes of sporting events
CS 229 FINAL PROJECT, AUTUMN 2014 1 Predicting National Basketball Association Winners Jasper Lin, Logan Short, and Vishnu Sundaresan Abstract We used National Basketball Associations box scores from 1991-1998
More informationBASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG
BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG GOAL OF PROJECT The goal is to predict the winners between college men s basketball teams competing in the 2018 (NCAA) s March
More informationA Novel Approach to Predicting the Results of NBA Matches
A Novel Approach to Predicting the Results of NBA Matches Omid Aryan Stanford University aryano@stanford.edu Ali Reza Sharafat Stanford University sharafat@stanford.edu Abstract The current paper presents
More informationLecture 10. Support Vector Machines (cont.)
Lecture 10. Support Vector Machines (cont.) COMP90051 Statistical Machine Learning Semester 2, 2017 Lecturer: Andrey Kan Copyright: University of Melbourne This lecture Soft margin SVM Intuition and problem
More informationThis file is part of the following reference:
This file is part of the following reference: Hancock, Timothy Peter (2006) Multivariate consensus trees: tree-based clustering and profiling for mixed data types. PhD thesis, James Cook University. Access
More informationOperational Risk Management: Preventive vs. Corrective Control
Operational Risk Management: Preventive vs. Corrective Control Yuqian Xu (UIUC) July 2018 Joint Work with Lingjiong Zhu and Michael Pinedo 1 Research Questions How to manage operational risk? How does
More informationAttacking and defending neural networks. HU Xiaolin ( 胡晓林 ) Department of Computer Science and Technology Tsinghua University, Beijing, China
Attacking and defending neural networks HU Xiaolin ( 胡晓林 ) Department of Computer Science and Technology Tsinghua University, Beijing, China Outline Background Attacking methods Defending methods 2 AI
More information2 When Some or All Labels are Missing: The EM Algorithm
CS769 Spring Advanced Natural Language Processing The EM Algorithm Lecturer: Xiaojin Zhu jerryzhu@cs.wisc.edu Given labeled examples (x, y ),..., (x l, y l ), one can build a classifier. If in addition
More informationJasmin Smajic 1, Christian Hafner 2, Jürg Leuthold 2, March 16, 2015 Introduction to Finite Element Method (FEM) Part 1 (2-D FEM)
Jasmin Smajic 1, Christian Hafner 2, Jürg Leuthold 2, March 16, 2015 Introduction to Finite Element Method (FEM) Part 1 (2-D FEM) 1 HSR - University of Applied Sciences of Eastern Switzerland Institute
More informationIvan Suarez Robles, Joseph Wu 1
Ivan Suarez Robles, Joseph Wu 1 Section 1: Introduction Mixed Martial Arts (MMA) is the fastest growing competitive sport in the world. Because the fighters engage in distinct martial art disciplines (boxing,
More informationConservation of Energy. Chapter 7 of Essential University Physics, Richard Wolfson, 3 rd Edition
Conservation of Energy Chapter 7 of Essential University Physics, Richard Wolfson, 3 rd Edition 1 Different Types of Force, regarding the Work they do. gravity friction 2 Conservative Forces BB WW cccccccc
More informationNCSS Statistical Software
Chapter 256 Introduction This procedure computes summary statistics and common non-parametric, single-sample runs tests for a series of n numeric, binary, or categorical data values. For numeric data,
More informationPREDICTING THE NCAA BASKETBALL TOURNAMENT WITH MACHINE LEARNING. The Ringer/Getty Images
PREDICTING THE NCAA BASKETBALL TOURNAMENT WITH MACHINE LEARNING A N D R E W L E V A N D O S K I A N D J O N A T H A N L O B O The Ringer/Getty Images THE TOURNAMENT MARCH MADNESS 68 teams (4 play-in games)
More informationEE582 Physical Design Automation of VLSI Circuits and Systems
EE Prof. Dae Hyun Kim School of Electrical Engineering and Computer Science Washington State University Routing Grid Routing Grid Routing Grid Routing Grid Routing Grid Routing Lee s algorithm (Maze routing)
More informationFunctions of Random Variables & Expectation, Mean and Variance
Functions of Random Variables & Expectation, Mean and Variance Kuan-Yu Chen ( 陳冠宇 ) @ TR-409, NTUST Functions of Random Variables 1 Given a random variables XX, one may generate other random variables
More informationCombining Experimental and Non-Experimental Design in Causal Inference
Combining Experimental and Non-Experimental Design in Causal Inference Kari Lock Morgan Department of Statistics Penn State University Rao Prize Conference May 12 th, 2017 A Tribute to Don Design trumps
More informationCS 7641 A (Machine Learning) Sethuraman K, Parameswaran Raman, Vijay Ramakrishnan
CS 7641 A (Machine Learning) Sethuraman K, Parameswaran Raman, Vijay Ramakrishnan Scenario 1: Team 1 scored 200 runs from their 50 overs, and then Team 2 reaches 146 for the loss of two wickets from their
More informationPredicting NBA Shots
Predicting NBA Shots Brett Meehan Stanford University https://github.com/brettmeehan/cs229 Final Project bmeehan2@stanford.edu Abstract This paper examines the application of various machine learning algorithms
More informationSimulating Major League Baseball Games
ABSTRACT Paper 2875-2018 Simulating Major League Baseball Games Justin Long, Slippery Rock University; Brad Schweitzer, Slippery Rock University; Christy Crute Ph.D, Slippery Rock University The game of
More informationCommunication Amid Uncertainty
Communication Amid Uncertainty Madhu Sudan Harvard University Based on joint works with Brendan Juba, Oded Goldreich, Adam Kalai, Sanjeev Khanna, Elad Haramaty, Jacob Leshno, Clement Canonne, Venkatesan
More informationMachine Learning Application in Aviation Safety
Machine Learning Application in Aviation Safety Surface Safety Metric MOR Classification Presented to: By: Date: ART Firdu Bati, PhD, FAA September, 2018 Agenda Surface Safety Metric (SSM) development
More informationChapter 10 Aggregate Demand I: Building the IS LM Model
Chapter 10 Aggregate Demand I: Building the IS LM Model Zhengyu Cai Ph.D. Institute of Development Southwestern University of Finance and Economics All rights reserved http://www.escience.cn/people/zhengyucai/index.html
More informationCommunication Amid Uncertainty
Communication Amid Uncertainty Madhu Sudan Harvard University Based on joint works with Brendan Juba, Oded Goldreich, Adam Kalai, Sanjeev Khanna, Elad Haramaty, Jacob Leshno, Clement Canonne, Venkatesan
More informationIntroduction to Genetics
Name: Introduction to Genetics Keystone Assessment Anchor: BIO.B.2.1.1: Describe and/or predict observed patterns of inheritance (i.e. dominant, recessive, co-dominance, incomplete dominance, sex-linked,
More informationDo New Bike Share Stations Increase Member Use?: A Quasi-Experimental Study
Do New Bike Share Stations Increase Member Use?: A Quasi-Experimental Study Jueyu Wang & Greg Lindsey Humphrey School of Public Affairs University of Minnesota Acknowledgement: NSF Sustainable Research
More informationB. AA228/CS238 Component
Abstract Two supervised learning methods, one employing logistic classification and another employing an artificial neural network, are used to predict the outcome of baseball postseason series, given
More informationA computer program that improves its performance at some task through experience.
1 A computer program that improves its performance at some task through experience. 2 Example: Learn to Diagnose Patients T: Diagnose tumors from images P: Percent of patients correctly diagnosed E: Pre
More informationThe Intrinsic Value of a Batted Ball Technical Details
The Intrinsic Value of a Batted Ball Technical Details Glenn Healey, EECS Department University of California, Irvine, CA 9617 Given a set of observed batted balls and their outcomes, we develop a method
More informationTie Breaking Procedure
Ohio Youth Basketball Tie Breaking Procedure The higher seeded team when two teams have the same record after completion of pool play will be determined by the winner of their head to head competition.
More informationMidterm Exam 1, section 2. Thursday, September hour, 15 minutes
San Francisco State University Michael Bar ECON 312 Fall 2018 Midterm Exam 1, section 2 Thursday, September 27 1 hour, 15 minutes Name: Instructions 1. This is closed book, closed notes exam. 2. You can
More informationImperfectly Shared Randomness in Communication
Imperfectly Shared Randomness in Communication Madhu Sudan Harvard Joint work with Clément Canonne (Columbia), Venkatesan Guruswami (CMU) and Raghu Meka (UCLA). 11/16/2016 UofT: ISR in Communication 1
More informationPredicting Results of March Madness Using the Probability Self-Consistent Method
International Journal of Sports Science 2015, 5(4): 139-144 DOI: 10.5923/j.sports.20150504.04 Predicting Results of March Madness Using the Probability Self-Consistent Method Gang Shen, Su Hua, Xiao Zhang,
More informationThe Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD
The Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD Outline Definition. Deriving the Estimates. Properties of the Estimates. Units of Measurement and Functional Form. Expected
More informationCS 221 PROJECT FINAL
CS 221 PROJECT FINAL STUART SY AND YUSHI HOMMA 1. INTRODUCTION OF TASK ESPN fantasy baseball is a common pastime for many Americans, which, coincidentally, defines a problem whose solution could potentially
More informationA Machine Learning Approach to Predicting Winning Patterns in Track Cycling Omnium
A Machine Learning Approach to Predicting Winning Patterns in Track Cycling Omnium Bahadorreza Ofoghi 1,2, John Zeleznikow 1, Clare MacMahon 1,andDanDwyer 2 1 Victoria University, Melbourne VIC 3000, Australia
More informationNeural Networks II. Chen Gao. Virginia Tech Spring 2019 ECE-5424G / CS-5824
Neural Networks II Chen Gao ECE-5424G / CS-5824 Virginia Tech Spring 2019 Neural Networks Origins: Algorithms that try to mimic the brain. What is this? A single neuron in the brain Input Output Slide
More informationPhysical Design of CMOS Integrated Circuits
Physical Design of CMOS Integrated Circuits Dae Hyun Kim EECS Washington State University References John P. Uyemura, Introduction to VLSI Circuits and Systems, 2002. Chapter 5 Goal Understand how to physically
More informationMachine Learning an American Pastime
Nikhil Bhargava, Andy Fang, Peter Tseng CS 229 Paper Machine Learning an American Pastime I. Introduction Baseball has been a popular American sport that has steadily gained worldwide appreciation in the
More informationNaïve Bayes. Robot Image Credit: Viktoriya Sukhanova 123RF.com
Naïve Bayes These slides were assembled by Eric Eaton, with grateful acknowledgement of the many others who made their course materials freely available online. Feel free to reuse or adapt these slides
More informationPOKEMON HACKS. Jodie Ashford Josh Baggott Chloe Barnes Jordan bird
POKEMON HACKS Jodie Ashford Josh Baggott Chloe Barnes Jordan bird Why pokemon? 1997 The Pokemon Problem an episode of the anime caused 685 children to have seizures Professor Graham Harding from Aston
More informationTitle: 4-Way-Stop Wait-Time Prediction Group members (1): David Held
Title: 4-Way-Stop Wait-Time Prediction Group members (1): David Held As part of my research in Sebastian Thrun's autonomous driving team, my goal is to predict the wait-time for a car at a 4-way intersection.
More informationNaïve Bayes. Robot Image Credit: Viktoriya Sukhanova 123RF.com
Naïve Bayes These slides were assembled by Byron Boots, with only minor modifications from Eric Eaton s slides and grateful acknowledgement to the many others who made their course materials freely available
More informationISyE 6414: Regression Analysis
ISyE 6414: Regression Analysis Lectures: MWF 8:00-10:30, MRDC #2404 Early five-week session; May 14- June 15 (8:00-9:10; 10-min break; 9:20-10:30) Instructor: Dr. Yajun Mei ( YA_JUNE MAY ) Email: ymei@isye.gatech.edu;
More informationBuilding an NFL performance metric
Building an NFL performance metric Seonghyun Paik (spaik1@stanford.edu) December 16, 2016 I. Introduction In current pro sports, many statistical methods are applied to evaluate player s performance and
More informationUsing Spatio-Temporal Data To Create A Shot Probability Model
Using Spatio-Temporal Data To Create A Shot Probability Model Eli Shayer, Ankit Goyal, Younes Bensouda Mourri June 2, 2016 1 Introduction Basketball is an invasion sport, which means that players move
More informationA Class of Regression Estimator with Cum-Dual Ratio Estimator as Intercept
International Journal of Probability and Statistics 015, 4(): 4-50 DOI: 10.593/j.ijps.015040.0 A Class of Regression Estimator with Cum-Dual Ratio Estimator as Intercept F. B. Adebola 1, N. A. Adegoke
More informationModeling the NCAA Tournament Through Bayesian Logistic Regression
Duquesne University Duquesne Scholarship Collection Electronic Theses and Dissertations 0 Modeling the NCAA Tournament Through Bayesian Logistic Regression Bryan Nelson Follow this and additional works
More informationTwo Machine Learning Approaches to Understand the NBA Data
Two Machine Learning Approaches to Understand the NBA Data Panagiotis Lolas December 14, 2017 1 Introduction In this project, I consider applications of machine learning in the analysis of nba data. To
More informationA team recommendation system and outcome prediction for the game of cricket
Journal of Sports Analytics 4 (2018) 263 273 DOI 10.3233/JSA-170196 IOS Press 263 A team recommendation system and outcome prediction for the game of cricket Sandesh Bananki Jayanth a,b,, Akas Anthony
More informationJPEG-Compatibility Steganalysis Using Block-Histogram of Recompression Artifacts
JPEG-Compatibility Steganalysis Using Block-Histogram of Recompression Artifacts Jan Kodovský, Jessica Fridrich May 16, 2012 / IH Conference 1 / 19 What is JPEG-compatibility steganalysis? Detects embedding
More informationGait Based Personal Identification System Using Rotation Sensor
Gait Based Personal Identification System Using Rotation Sensor Soumik Mondal, Anup Nandy, Pavan Chakraborty, G. C. Nandi Robotics & AI Lab, Indian Institute of Information Technology Allahabad, India
More informationPredicting Momentum Shifts in NBA Games
Predicting Momentum Shifts in NBA Games Whitney LaRow, Brian Mittl, Vijay Singh Stanford University December 11, 2015 1 Introduction During the first round of the 2015 NBA playoffs, the eventual champions,
More informationPredicting the Total Number of Points Scored in NFL Games
Predicting the Total Number of Points Scored in NFL Games Max Flores (mflores7@stanford.edu), Ajay Sohmshetty (ajay14@stanford.edu) CS 229 Fall 2014 1 Introduction Predicting the outcome of National Football
More informationDATA MINING ON CRICKET DATA SET FOR PREDICTING THE RESULTS. Sushant Murdeshwar
DATA MINING ON CRICKET DATA SET FOR PREDICTING THE RESULTS by Sushant Murdeshwar A Project Report Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science
More informationHuman Performance Evaluation
Human Performance Evaluation Minh Nguyen, Liyue Fan, Luciano Nocera, Cyrus Shahabi minhnngu@usc.edu --O-- Integrated Media Systems Center University of Southern California 1 2 Motivating Application 8.2
More informationAvailable online at ScienceDirect. Procedia Manufacturing 3 (2015 )
Available online at www.sciencedirect.com ScienceDirect Procedia Manufacturing 3 (2015 ) 858 865 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences,
More informationAddition and Subtraction of Rational Expressions
RT.3 Addition and Subtraction of Rational Expressions Many real-world applications involve adding or subtracting algebraic fractions. Similarly as in the case of common fractions, to add or subtract algebraic
More informationMobility Detection Using Everyday GSM Traces
Mobility Detection Using Everyday GSM Traces Timothy Sohn et al Philip Cootey pcootey@wpi.edu (03/22/2011) Mobility Detection High level activity discerned from course Grained GSM Data provides immediate
More informationPredicting Horse Racing Results with TensorFlow
Predicting Horse Racing Results with TensorFlow LYU 1703 LIU YIDE WANG ZUOYANG News CUHK Professor, Gu Mingao, wins 50 MILLIONS dividend using his sure-win statistical strategy. News AlphaGO defeats human
More informationConfigurable Test-Goal Set Partitioning for Directed Multi- Goal Test Generation
Configurable Test-Goal Set Partitioning for Directed Multi- Goal Test Generation Malte Lochau (TU Darmstadt) Joint work with: Sven Apel, Johannes Bürdek, Dirk Beyer, Andreas Holzer, Andreas Stahlbauer
More informationEnvironmental Science: An Indian Journal
Environmental Science: An Indian Journal Research Vol 14 Iss 1 Flow Pattern and Liquid Holdup Prediction in Multiphase Flow by Machine Learning Approach Chandrasekaran S *, Kumar S Petroleum Engineering
More informationCS 528 Mobile and Ubiquitous Computing Lecture 7a: Applications of Activity Recognition + Machine Learning for Ubiquitous Computing.
CS 528 Mobile and Ubiquitous Computing Lecture 7a: Applications of Activity Recognition + Machine Learning for Ubiquitous Computing Emmanuel Agu Applications of Activity Recognition Recall: Activity Recognition
More informationPredicting the NCAA Men s Basketball Tournament with Machine Learning
Predicting the NCAA Men s Basketball Tournament with Machine Learning Andrew Levandoski and Jonathan Lobo CS 2750: Machine Learning Dr. Kovashka 25 April 2017 Abstract As the popularity of the NCAA Men
More informationThe Effect of Public Sporting Expenditures on Medal Share at the Summer Olympic Games: A Study of the Differential Impact by Sport and Gender
Dickinson College Dickinson Scholar Student Honors Theses By Year Student Honors Theses 5-21-2017 The Effect of Public Sporting Expenditures on Medal Share at the Summer Olympic Games: A Study of the Differential
More informationSimplifying Radical Expressions and the Distance Formula
1 RD. Simplifying Radical Expressions and the Distance Formula In the previous section, we simplified some radical expressions by replacing radical signs with rational exponents, applying the rules of
More informationJunction design rules for urban traffic networks
Junction design rules for urban traffic networks Erwin Bezembinder European Transport Conference Frankfurt, 1 October 2014 Involved Erwin Bezembinder Luc Wismans Eric van Berkum Junction design challenge
More informationPredicting the use of the sacrifice bunt in Major League Baseball BUDT 714 May 10, 2007
Predicting the use of the sacrifice bunt in Major League Baseball BUDT 714 May 10, 2007 Group 6 Charles Gallagher Brian Gilbert Neelay Mehta Chao Rao Executive Summary Background When a runner is on-base
More informationPre-Kindergarten 2017 Summer Packet. Robert F Woodall Elementary
Pre-Kindergarten 2017 Summer Packet Robert F Woodall Elementary In the fall, on your child s testing day, please bring this packet back for a special reward that will be awarded to your child for completion
More informationCOMP Intro to Logic for Computer Scientists. Lecture 13
COMP 1002 Intro to Logic for Computer Scientists Lecture 13 B 5 2 J Admin stuff Assignments schedule? Split a2 and a3 in two (A2,3,4,5), 5% each. A2 due Feb 17 th. Midterm date? March 2 nd. No office hour
More informationCT4510: Computer Graphics. Transformation BOCHANG MOON
CT4510: Computer Graphics Transformation BOCHANG MOON 2D Translation Transformations such as rotation and scale can be represented using a matrix M ee. gg., MM = SSSS xx = mm 11 xx + mm 12 yy yy = mm 21
More informationSupport Vector Machines: Optimization of Decision Making. Christopher Katinas March 10, 2016
Support Vector Machines: Optimization of Decision Making Christopher Katinas March 10, 2016 Overview Background of Support Vector Machines Segregation Functions/Problem Statement Methodology Training/Testing
More informationOperations on Radical Expressions; Rationalization of Denominators
0 RD. 1 2 2 2 2 2 2 2 Operations on Radical Expressions; Rationalization of Denominators Unlike operations on fractions or decimals, sums and differences of many radicals cannot be simplified. For instance,
More informationModelling the distribution of first innings runs in T20 Cricket
Modelling the distribution of first innings runs in T20 Cricket James Kirkby The joy of smoothing James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 1 / 22
More informationProduct Decomposition in Supply Chain Planning
Mario R. Eden, Marianthi Ierapetritou and Gavin P. Towler (Editors) Proceedings of the 13 th International Symposium on Process Systems Engineering PSE 2018 July 1-5, 2018, San Diego, California, USA 2018
More informationCopyright 2017 by Glenn Daniel Sidle. All Rights Reserved
ABSTRACT SIDLE, GLENN DANIEL. Using Multi-Class Machine Learning Methods to Predict Major League Baseball Pitches. (Under the direction of Hien Tran.) As the field of machine learning and its applications
More informationTSP at isolated intersections: Some advances under simulation environment
TSP at isolated intersections: Some advances under simulation environment Zhengyao Yu Vikash V. Gayah Eleni Christofa TESC 2018 December 5, 2018 Overview Motivation Problem introduction Assumptions Formation
More informationCoaches, Parents, Players and Fans
P.O. Box 865 * Lancaster, OH 43130 * 740-808-0380 * www.ohioyouthbasketball.com Coaches, Parents, Players and Fans Sunday s Championship Tournament in Boys Grades 5th thru 10/11th will be conducted in
More informationCOMPLETING THE RESULTS OF THE 2013 BOSTON MARATHON
COMPLETING THE RESULTS OF THE 2013 BOSTON MARATHON Dorit Hammerling 1, Matthew Cefalu 2, Jessi Cisewski 3, Francesca Dominici 2, Giovanni Parmigiani 2,4, Charles Paulson 5, Richard Smith 1,6 1 Statistical
More informationAn agent-based methodological approach for modeling travelers behavior on modal shift: The case of inland transport in Denmark.
An agent-based methodological approach for modeling travelers behavior on modal shift: The case of inland transport in Denmark ETSAP Workshop Zurich, 11 th -12 th Dec 2017 ABMoS-DK Mohammad Ahanchian PostDoc
More informationUsing Multi-Class Classification Methods to Predict Baseball Pitch Types
Using Multi-Class Classification Methods to Predict Baseball Pitch Types GLENN SIDLE, HIEN TRAN Department of Applied Mathematics North Carolina State University 2108 SAS Hall, Box 8205 Raleigh, NC 27695
More informationAn Artificial Neural Network-based Prediction Model for Underdog Teams in NBA Matches
An Artificial Neural Network-based Prediction Model for Underdog Teams in NBA Matches Paolo Giuliodori University of Camerino, School of Science and Technology, Computer Science Division, Italy paolo.giuliodori@unicam.it
More informationSmart-Walk: An Intelligent Physiological Monitoring System for Smart Families
Smart-Walk: An Intelligent Physiological Monitoring System for Smart Families P. Sundaravadivel 1, S. P. Mohanty 2, E. Kougianos 3, V. P. Yanambaka 4, and M. K. Ganapathiraju 5 University of North Texas,
More informationSPATIAL STATISTICS A SPATIAL ANALYSIS AND COMPARISON OF NBA PLAYERS. Introduction
A SPATIAL ANALYSIS AND COMPARISON OF NBA PLAYERS KELLIN RUMSEY Introduction The 2016 National Basketball Association championship featured two of the leagues biggest names. The Golden State Warriors Stephen
More informationPredicting Tennis Match Outcomes Through Classification Shuyang Fang CS074 - Dartmouth College
Predicting Tennis Match Outcomes Through Classification Shuyang Fang CS074 - Dartmouth College Introduction The governing body of men s professional tennis is the Association of Tennis Professionals or
More informationObject Recognition. Selim Aksoy. Bilkent University
Image Classification and Object Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Image classification Image (scene) classification is a fundamental
More informationConfidence Interval Notes Calculating Confidence Intervals
Confidence Interval Notes Calculating Confidence Intervals Calculating One-Population Mean Confidence Intervals for Quantitative Data It is always best to use a computer program to make these calculations,
More informationc 2016 Arash Khatibi
c 2016 Arash Khatibi MODELING THE WINNING SEED DISTRIBUTION OF THE NCAA BASKETBALL TOURNAMENT BY ARASH KHATIBI THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science
More informationFun Neural Net Demo Site. CS 188: Artificial Intelligence. N-Layer Neural Network. Multi-class Softmax Σ >0? Deep Learning II
Fun Neural Net Demo Site CS 188: Artificial Intelligence Demo-site: http://playground.tensorflow.org/ Deep Learning II Instructors: Pieter Abbeel & Anca Dragan --- University of California, Berkeley [These
More information