Introduction to NLP. Text Classification
|
|
- Mark Wilkinson
- 6 years ago
- Views:
Transcription
1 NLP
2 Introduction to NLP Text Classification
3 Classification Assigning documents to predefined categories topics, languages, users A given set of classes C Given x, determine its class in C Hierarchical vs. flat Overlapping (soft vs non-overlapping (hard
4 Classification Ideas: manual classification using rules e.g., Columbia AND University à Education Columbia AND South Carolina à Geography Popular techniques generative (-nn, Naïve Bayes vs. discriminative (SVM, regression Generative model joint prob p(x,y and use Bayesian prediction to compute p(y x Discriminative model p(y x directly.
5 Representations or Document Classification (And Clustering Typically: vector-based Words: cat, dog, etc. eatures: document length, author name, etc. Each document is represented as a vector in an n-dimensional space Similar documents appear nearby in the vector space (distance measures are needed
6 Naïve Bayesian classifiers Naïve Bayesian classifier Assuming statistical independence eatures = words (or phrases typically, ( (,..., (,..., (, P C d P C d P C d P = = = = j j j j P C d P C d P C d P ( ( (,..., (
7 Issues with Naïve Bayes Where do we get the values P ( d C use maximum lielihood estimation (N i /N Same for the conditionals these are based on a multinomial generator and the MLE estimator is (T ji /ΣT ji Smoothing is needed why Laplace smoothing ((T ji +1/Σ(T ji +1 Implementation how to avoid floating point underflow
8 Spam Recognition Return-Path: X-Sieve: CMU Sieve 2.2 rom: "Ibrahim Galadima" Reply-To: To: Subject: Gooday DEAR SIR UNDS OR INVESTMENTS THIS LETTER MAY COME TO YOU AS A SURPRISE SINCE I HAD NO PREVIOUS CORRESPONDENCE WITH YOU I AM THE CHAIRMAN TENDER BOARD O INDEPENDENT NATIONAL ELECTORAL COMMISSION INEC I GOT YOUR CONTACT IN THE COURSE O MY SEARCH OR A RELIABLE PERSON WITH WHOM TO HANDLE A VERY CONIDENTIAL TRANSACTION INVOLVING THE! TRANSER O UND VALUED AT TWENTY ONE MILLION SIX HUNDRED THOUSAND UNITED STATES DOLLARS US$20M TO A SAE OREIGN ACCOUNT
9 SpamAssassin Examples: body Incorporates a tracing ID number body HTML and text parts are different header Date: is 3 to 6 hours before Received: date body HTML font size is huge header Attempt to obfuscate words in Subject: header Subject =~ /^urgent(?:[\s\w]*(dollar.{1,40} (?:alert response assistance proposal reply warning noti(?:ce fication greeting matter/i
10 eature Selection: The Χ 2 Test or a term t: I t 0 1 C C=class, i t = feature Testing for independence: P(C=0,I t =0 should be equal to P(C=0 P(I t =0 P(C=0 = ( /n P(C=1 = 1-P(C=0 = ( /n P(I t =0 = ( 00 +K 10 /n P(I t =1 = 1-P(I t =0 = ( /n
11 eature Selection: The Χ 2 Test Χ 2 = ( n( 11 ( ( ( High values of Χ 2 indicate lower belief in independence. In practice, compute Χ 2 for all words and pic the top among them.
12 eature Selection: Mutual Information No document length scaling is needed Documents are assumed to be generated according to the multinomial model Measures amount of information: if the distribution is the same as the bacground distribution, then MI=0 X = word; Y = class MI( X, Y = P( x, ylog P( x, y x y P( x P( y
13 Well-nown Datasets 20 newsgroups Reuters reuters21578/ Cats: grain, acquisitions, corn, crude, wheat, trade WebKB course, student, faculty, staff, project, dept, other RCV1 Larger Reuters corpus
14 Evaluation Of Text Classification Microaveraging average over classes Macroaveraging uses pooled table
15 Vector Space Classification x2 topic2 topic1 x1
16 Decision Surfaces x2 topic2 topic1 x1
17 Decision Trees x2 topic2 topic1 x1
18 Linear Boundary x2 topic2 topic1 x1
19 Vector Space Classifiers Using centroids Boundary line that is equidistant from two centroids
20 Linear Separators Two-dimensional line: w 1 x 1 +w 2 x 2 =b is the linear separator w 1 x 1 +w 2 x 2 >b for the positive class In n-dimensional spaces:! w T! x = b
21 Decision Boundary x2 w! topic2 topic1 x1
22 Example Bias b=0 Document is A D E H Its score will be 0.6*1+0.4*1+0.4*1+(-0.5*1 = 0.9>0 w i x i w i x i 0.6 A -0.7 G 0.5 B -0.5 H 0.5 C -0.3 I 0.4 D -0.2 J 0.4 E -0.2 K L
23 Perceptron Algorithm Input: Algorithm: Output: R R = η 1,1} {,,,,...,(, (( i N n n y x y x y x S!!! END END 1 0 ( I 1TO OR 0 0, = + = = = = + x y w w x w y n i w i i i i!!!!!!! η w!
24 [Example from Chris Bishop]
25 Generative Models: nn Assign each element to the closest cluster K-nearest neighbors score( c, d q = b Very easy to program Issues: choosing, b? Demo: c + d NN ( s( d d q q, d
26 NLP
Generative Models for Classification
Generative Models for Classification CS4780/5780 Machine Learning Fall 2014 Thorsten Joachims Cornell University Reading: Mitchell, Chapter 6.9-6.10 Duda, Hart & Stork, Pages 20-39 Generative vs. Discriminative
More informationGenerative Models. CS4780/5780 Machine Learning Fall Thorsten Joachims Cornell University
Generative Models CS4780/5780 Machine Learning Fall 2012 Thorsten Joachims Cornell University Reading: Mitchell, Chapter 6.9-6.10 Duda, Hart & Stork, Pages 20-39 Bayes decision rule Bayes theorem Generative
More informationText classification II CE-324: Modern Information Retrieval Sharif University of Technology
Text classification II CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2016 Some slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More informationInformation Retrieval and Organisation
Information Retrieval and Organisation Chapter 13 Text Classification and Naïve Bayes Dell Zhang Birkbeck, University of London Motivation Relevance Feedback revisited The user marks a number of documents
More informationText classification II CE-324: Modern Information Retrieval Sharif University of Technology
Text classification II CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2017 Some slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More informationNaïve Bayes. Vibhav Gogate The University of Texas at Dallas
Naïve Bayes Vibhav Gogate The University of Texas at Dallas Supervised Learning of Classifiers Find f Given: Training set {(x i, y i ) i = 1 n} Find: A good approximation to f : X Y Examples: what are
More informationIntelligent Systems (AI-2)
Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 19 Oct, 23, 2015 Slide Sources Raymond J. Mooney University of Texas at Austin D. Koller, Stanford CS - Probabilistic Graphical Models D. Page,
More informationIntelligent Systems (AI-2)
Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 19 Oct, 24, 2016 Slide Sources Raymond J. Mooney University of Texas at Austin D. Koller, Stanford CS - Probabilistic Graphical Models D. Page,
More informationText Classification and Naïve Bayes
Text Classification and Naïve Bayes The Task of Text Classification Many slides are adapted from slides by Dan Jurafsky Is this spam? Who wrote which Federalist papers? 1787-8: anonymous essays try to
More informationGaussian Models
Gaussian Models ddebarr@uw.edu 2016-04-28 Agenda Introduction Gaussian Discriminant Analysis Inference Linear Gaussian Systems The Wishart Distribution Inferring Parameters Introduction Gaussian Density
More information9/12/17. Types of learning. Modeling data. Supervised learning: Classification. Supervised learning: Regression. Unsupervised learning: Clustering
Types of learning Modeling data Supervised: we know input and targets Goal is to learn a model that, given input data, accurately predicts target data Unsupervised: we know the input only and want to make
More informationnaive bayes document classification
naive bayes document classification October 31, 2018 naive bayes document classification 1 / 50 Overview 1 Text classification 2 Naive Bayes 3 NB theory 4 Evaluation of TC naive bayes document classification
More informationA REVIEW ARTICLE ON NAIVE BAYES CLASSIFIER WITH VARIOUS SMOOTHING TECHNIQUES
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 10, October 2014,
More informationClassifying Documents with Poisson Mixtures
Classifying Documents with Poisson Mixtures Hiroshi Ogura, Hiromi Amano, Masato Kondo Department of Information Science, Faculty of Arts and Sciences at Fujiyoshida, Showa University, 4562 Kamiyoshida,
More informationProbabilistic classification CE-717: Machine Learning Sharif University of Technology. M. Soleymani Fall 2016
Probabilistic classification CE-717: Machine Learning Sharif University of Technology M. Soleymani Fall 2016 Topics Probabilistic approach Bayes decision theory Generative models Gaussian Bayes classifier
More informationGenerative Models for Discrete Data
Generative Models for Discrete Data ddebarr@uw.edu 2016-04-21 Agenda Bayesian Concept Learning Beta-Binomial Model Dirichlet-Multinomial Model Naïve Bayes Classifiers Bayesian Concept Learning Numbers
More informationMachine Learning for natural language processing
Machine Learning for natural language processing Classification: Naive Bayes Laura Kallmeyer Heinrich-Heine-Universität Düsseldorf Summer 2016 1 / 20 Introduction Classification = supervised method for
More informationNaïve Bayes Classifiers and Logistic Regression. Doug Downey Northwestern EECS 349 Winter 2014
Naïve Bayes Classifiers and Logistic Regression Doug Downey Northwestern EECS 349 Winter 2014 Naïve Bayes Classifiers Combines all ideas we ve covered Conditional Independence Bayes Rule Statistical Estimation
More informationChapter 6 Classification and Prediction (2)
Chapter 6 Classification and Prediction (2) Outline Classification and Prediction Decision Tree Naïve Bayes Classifier Support Vector Machines (SVM) K-nearest Neighbors Accuracy and Error Measures Feature
More informationText Categorization CSE 454. (Based on slides by Dan Weld, Tom Mitchell, and others)
Text Categorization CSE 454 (Based on slides by Dan Weld, Tom Mitchell, and others) 1 Given: Categorization A description of an instance, x X, where X is the instance language or instance space. A fixed
More informationCSE446: Naïve Bayes Winter 2016
CSE446: Naïve Bayes Winter 2016 Ali Farhadi Slides adapted from Carlos Guestrin, Dan Klein, Luke ZeIlemoyer Supervised Learning: find f Given: Training set {(x i, y i ) i = 1 n} Find: A good approximanon
More informationProbability Review and Naïve Bayes
Probability Review and Naïve Bayes Instructor: Alan Ritter Some slides adapted from Dan Jurfasky and Brendan O connor What is Probability? The probability the coin will land heads is 0.5 Q: what does this
More informationMidterm: CS 6375 Spring 2018
Midterm: CS 6375 Spring 2018 The exam is closed book (1 cheat sheet allowed). Answer the questions in the spaces provided on the question sheets. If you run out of room for an answer, use an additional
More informationCS 188: Artificial Intelligence. Machine Learning
CS 188: Artificial Intelligence Review of Machine Learning (ML) DISCLAIMER: It is insufficient to simply study these slides, they are merely meant as a quick refresher of the high-level ideas covered.
More informationPattern Recognition and Machine Learning
Christopher M. Bishop Pattern Recognition and Machine Learning ÖSpri inger Contents Preface Mathematical notation Contents vii xi xiii 1 Introduction 1 1.1 Example: Polynomial Curve Fitting 4 1.2 Probability
More informationCSE 546 Final Exam, Autumn 2013
CSE 546 Final Exam, Autumn 0. Personal info: Name: Student ID: E-mail address:. There should be 5 numbered pages in this exam (including this cover sheet).. You can use any material you brought: any book,
More informationText Mining. Dr. Yanjun Li. Associate Professor. Department of Computer and Information Sciences Fordham University
Text Mining Dr. Yanjun Li Associate Professor Department of Computer and Information Sciences Fordham University Outline Introduction: Data Mining Part One: Text Mining Part Two: Preprocessing Text Data
More informationUNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning Final, Fall 2013
UNIVERSITY of PENNSYLVANIA CIS 520: Machine Learning Final, Fall 2013 Exam policy: This exam allows two one-page, two-sided cheat sheets; No other materials. Time: 2 hours. Be sure to write your name and
More informationMachine Learning. Hal Daumé III. Computer Science University of Maryland CS 421: Introduction to Artificial Intelligence 8 May 2012
Machine Learning Hal Daumé III Computer Science University of Maryland me@hal3.name CS 421 Introduction to Artificial Intelligence 8 May 2012 g 1 Many slides courtesy of Dan Klein, Stuart Russell, or Andrew
More informationClassification Algorithms
Classification Algorithms UCSB 290N, 2015. T. Yang Slides based on R. Mooney UT Austin 1 Table of Content roblem Definition Rocchio K-nearest neighbor case based Bayesian algorithm Decision trees 2 Given:
More informationProbabilistic modeling. The slides are closely adapted from Subhransu Maji s slides
Probabilistic modeling The slides are closely adapted from Subhransu Maji s slides Overview So far the models and algorithms you have learned about are relatively disconnected Probabilistic modeling framework
More informationMachine Learning Overview
Machine Learning Overview Sargur N. Srihari University at Buffalo, State University of New York USA 1 Outline 1. What is Machine Learning (ML)? 2. Types of Information Processing Problems Solved 1. Regression
More informationPart-of-Speech Tagging + Neural Networks 3: Word Embeddings CS 287
Part-of-Speech Tagging + Neural Networks 3: Word Embeddings CS 287 Review: Neural Networks One-layer multi-layer perceptron architecture, NN MLP1 (x) = g(xw 1 + b 1 )W 2 + b 2 xw + b; perceptron x is the
More informationMachine Learning in a Nutshell. Data Model Performance Measure Machine Learner
Machine Learning 1 Machine Learning in a Nutshell Data Model Performance Measure Machine Learner 2 Machine Learning in a Nutshell Data Model Performance Measure Machine Learner Data with a5ributes ID A1
More informationMachine learning comes from Bayesian decision theory in statistics. There we want to minimize the expected value of the loss function.
Bayesian learning: Machine learning comes from Bayesian decision theory in statistics. There we want to minimize the expected value of the loss function. Let y be the true label and y be the predicted
More informationCS540 ANSWER SHEET
CS540 ANSWER SHEET Name Email 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 1 2 Final Examination CS540-1: Introduction to Artificial Intelligence Fall 2016 20 questions, 5 points
More informationThe Bayes classifier
The Bayes classifier Consider where is a random vector in is a random variable (depending on ) Let be a classifier with probability of error/risk given by The Bayes classifier (denoted ) is the optimal
More informationCS145: INTRODUCTION TO DATA MINING
CS145: INTRODUCTION TO DATA MINING Text Data: Topic Model Instructor: Yizhou Sun yzsun@cs.ucla.edu December 4, 2017 Methods to be Learnt Vector Data Set Data Sequence Data Text Data Classification Clustering
More informationProbabilistic Graphical Models
Probabilistic Graphical Models David Sontag New York University Lecture 4, February 16, 2012 David Sontag (NYU) Graphical Models Lecture 4, February 16, 2012 1 / 27 Undirected graphical models Reminder
More informationClick Prediction and Preference Ranking of RSS Feeds
Click Prediction and Preference Ranking of RSS Feeds 1 Introduction December 11, 2009 Steven Wu RSS (Really Simple Syndication) is a family of data formats used to publish frequently updated works. RSS
More informationNeural Networks. Single-layer neural network. CSE 446: Machine Learning Emily Fox University of Washington March 10, /9/17
3/9/7 Neural Networks Emily Fox University of Washington March 0, 207 Slides adapted from Ali Farhadi (via Carlos Guestrin and Luke Zettlemoyer) Single-layer neural network 3/9/7 Perceptron as a neural
More informationCOMS 4771 Introduction to Machine Learning. Nakul Verma
COMS 4771 Introduction to Machine Learning Nakul Verma Announcements HW1 due next lecture Project details are available decide on the group and topic by Thursday Last time Generative vs. Discriminative
More informationCS 188: Artificial Intelligence Spring Today
CS 188: Artificial Intelligence Spring 2006 Lecture 9: Naïve Bayes 2/14/2006 Dan Klein UC Berkeley Many slides from either Stuart Russell or Andrew Moore Bayes rule Today Expectations and utilities Naïve
More informationClassification Algorithms
Classification Algorithms UCSB 293S, 2017. T. Yang Slides based on R. Mooney UT Austin 1 Table of Content Problem Definition Rocchio K-nearest neighbor case based Bayesian algorithm Decision trees 2 Classification
More informationIntro. ANN & Fuzzy Systems. Lecture 15. Pattern Classification (I): Statistical Formulation
Lecture 15. Pattern Classification (I): Statistical Formulation Outline Statistical Pattern Recognition Maximum Posterior Probability (MAP) Classifier Maximum Likelihood (ML) Classifier K-Nearest Neighbor
More informationOn a New Model for Automatic Text Categorization Based on Vector Space Model
On a New Model for Automatic Text Categorization Based on Vector Space Model Makoto Suzuki, Naohide Yamagishi, Takashi Ishida, Masayuki Goto and Shigeichi Hirasawa Faculty of Information Science, Shonan
More informationMachine Learning for Signal Processing Bayes Classification and Regression
Machine Learning for Signal Processing Bayes Classification and Regression Instructor: Bhiksha Raj 11755/18797 1 Recap: KNN A very effective and simple way of performing classification Simple model: For
More informationMidterm Review CS 7301: Advanced Machine Learning. Vibhav Gogate The University of Texas at Dallas
Midterm Review CS 7301: Advanced Machine Learning Vibhav Gogate The University of Texas at Dallas Supervised Learning Issues in supervised learning What makes learning hard Point Estimation: MLE vs Bayesian
More informationCategorization ANLP Lecture 10 Text Categorization with Naive Bayes
1 Categorization ANLP Lecture 10 Text Categorization with Naive Bayes Sharon Goldwater 6 October 2014 Important task for both humans and machines object identification face recognition spoken word recognition
More informationANLP Lecture 10 Text Categorization with Naive Bayes
ANLP Lecture 10 Text Categorization with Naive Bayes Sharon Goldwater 6 October 2014 Categorization Important task for both humans and machines 1 object identification face recognition spoken word recognition
More informationFINAL: CS 6375 (Machine Learning) Fall 2014
FINAL: CS 6375 (Machine Learning) Fall 2014 The exam is closed book. You are allowed a one-page cheat sheet. Answer the questions in the spaces provided on the question sheets. If you run out of room for
More informationIntroduction to Machine Learning Midterm Exam
10-701 Introduction to Machine Learning Midterm Exam Instructors: Eric Xing, Ziv Bar-Joseph 17 November, 2015 There are 11 questions, for a total of 100 points. This exam is open book, open notes, but
More informationClassification, Linear Models, Naïve Bayes
Classification, Linear Models, Naïve Bayes CMSC 470 Marine Carpuat Slides credit: Dan Jurafsky & James Martin, Jacob Eisenstein Today Text classification problems and their evaluation Linear classifiers
More informationNumerical Learning Algorithms
Numerical Learning Algorithms Example SVM for Separable Examples.......................... Example SVM for Nonseparable Examples....................... 4 Example Gaussian Kernel SVM...............................
More informationMachine Learning (CS 567) Lecture 2
Machine Learning (CS 567) Lecture 2 Time: T-Th 5:00pm - 6:20pm Location: GFS118 Instructor: Sofus A. Macskassy (macskass@usc.edu) Office: SAL 216 Office hours: by appointment Teaching assistant: Cheol
More informationLinear Models for Classification: Discriminative Learning (Perceptron, SVMs, MaxEnt)
Linear Models for Classification: Discriminative Learning (Perceptron, SVMs, MaxEnt) Nathan Schneider (some slides borrowed from Chris Dyer) ENLP 12 February 2018 23 Outline Words, probabilities Features,
More informationDATA MINING LECTURE 10
DATA MINING LECTURE 10 Classification Nearest Neighbor Classification Support Vector Machines Logistic Regression Naïve Bayes Classifier Supervised Learning 10 10 Illustrating Classification Task Tid Attrib1
More informationClassification CE-717: Machine Learning Sharif University of Technology. M. Soleymani Fall 2012
Classification CE-717: Machine Learning Sharif University of Technology M. Soleymani Fall 2012 Topics Discriminant functions Logistic regression Perceptron Generative models Generative vs. discriminative
More informationMachine Learning: Assignment 1
10-701 Machine Learning: Assignment 1 Due on Februrary 0, 014 at 1 noon Barnabas Poczos, Aarti Singh Instructions: Failure to follow these directions may result in loss of points. Your solutions for this
More informationNatural Language Processing (CSEP 517): Text Classification
Natural Language Processing (CSEP 517): Text Classification Noah Smith c 2017 University of Washington nasmith@cs.washington.edu April 10, 2017 1 / 71 To-Do List Online quiz: due Sunday Read: Jurafsky
More informationInterpreting Deep Classifiers
Ruprecht-Karls-University Heidelberg Faculty of Mathematics and Computer Science Seminar: Explainable Machine Learning Interpreting Deep Classifiers by Visual Distillation of Dark Knowledge Author: Daniela
More informationMachine Learning, Fall 2012 Homework 2
0-60 Machine Learning, Fall 202 Homework 2 Instructors: Tom Mitchell, Ziv Bar-Joseph TA in charge: Selen Uguroglu email: sugurogl@cs.cmu.edu SOLUTIONS Naive Bayes, 20 points Problem. Basic concepts, 0
More informationClassification & Information Theory Lecture #8
Classification & Information Theory Lecture #8 Introduction to Natural Language Processing CMPSCI 585, Fall 2007 University of Massachusetts Amherst Andrew McCallum Today s Main Points Automatically categorizing
More informationNaïve Bayes Introduction to Machine Learning. Matt Gormley Lecture 3 September 14, Readings: Mitchell Ch Murphy Ch.
School of Computer Science 10-701 Introduction to Machine Learning aïve Bayes Readings: Mitchell Ch. 6.1 6.10 Murphy Ch. 3 Matt Gormley Lecture 3 September 14, 2016 1 Homewor 1: due 9/26/16 Project Proposal:
More informationCS 446 Machine Learning Fall 2016 Nov 01, Bayesian Learning
CS 446 Machine Learning Fall 206 Nov 0, 206 Bayesian Learning Professor: Dan Roth Scribe: Ben Zhou, C. Cervantes Overview Bayesian Learning Naive Bayes Logistic Regression Bayesian Learning So far, we
More informationLogistic Regression Introduction to Machine Learning. Matt Gormley Lecture 8 Feb. 12, 2018
10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Logistic Regression Matt Gormley Lecture 8 Feb. 12, 2018 1 10-601 Introduction
More informationChap 1. Overview of Statistical Learning (HTF, , 2.9) Yongdai Kim Seoul National University
Chap 1. Overview of Statistical Learning (HTF, 2.1-2.6, 2.9) Yongdai Kim Seoul National University 0. Learning vs Statistical learning Learning procedure Construct a claim by observing data or using logics
More informationEstimating Parameters
Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University September 13, 2012 Today: Bayes Classifiers Naïve Bayes Gaussian Naïve Bayes Readings: Mitchell: Naïve Bayes
More informationMore on HMMs and other sequence models. Intro to NLP - ETHZ - 18/03/2013
More on HMMs and other sequence models Intro to NLP - ETHZ - 18/03/2013 Summary Parts of speech tagging HMMs: Unsupervised parameter estimation Forward Backward algorithm Bayesian variants Discriminative
More informationCSE546: Naïve Bayes Winter 2012
CSE546: Naïve Bayes Winter 2012 Luke Ze=lemoyer Slides adapted from Carlos Guestrin and Dan Klein Supervised Learning: find f Given: Training set {(x i, y i ) i = 1 n} Find: A good approximamon to f :
More informationCS 188: Artificial Intelligence Fall 2008
CS 188: Artificial Intelligence Fall 2008 Lecture 23: Perceptrons 11/20/2008 Dan Klein UC Berkeley 1 General Naïve Bayes A general naive Bayes model: C E 1 E 2 E n We only specify how each feature depends
More informationGeneral Naïve Bayes. CS 188: Artificial Intelligence Fall Example: Overfitting. Example: OCR. Example: Spam Filtering. Example: Spam Filtering
CS 188: Artificial Intelligence Fall 2008 General Naïve Bayes A general naive Bayes model: C Lecture 23: Perceptrons 11/20/2008 E 1 E 2 E n Dan Klein UC Berkeley We only specify how each feature depends
More informationSUPERVISED LEARNING: INTRODUCTION TO CLASSIFICATION
SUPERVISED LEARNING: INTRODUCTION TO CLASSIFICATION 1 Outline Basic terminology Features Training and validation Model selection Error and loss measures Statistical comparison Evaluation measures 2 Terminology
More informationDay 5: Generative models, structured classification
Day 5: Generative models, structured classification Introduction to Machine Learning Summer School June 18, 2018 - June 29, 2018, Chicago Instructor: Suriya Gunasekar, TTI Chicago 22 June 2018 Linear regression
More informationGaussian and Linear Discriminant Analysis; Multiclass Classification
Gaussian and Linear Discriminant Analysis; Multiclass Classification Professor Ameet Talwalkar Slide Credit: Professor Fei Sha Professor Ameet Talwalkar CS260 Machine Learning Algorithms October 13, 2015
More informationBayes Theorem & Naïve Bayes. (some slides adapted from slides by Massimo Poesio, adapted from slides by Chris Manning)
Bayes Theorem & Naïve Bayes (some slides adapted from slides by Massimo Poesio, adapted from slides by Chris Manning) Review: Bayes Theorem & Diagnosis P( a b) Posterior Likelihood Prior P( b a) P( a)
More informationIntroduction to AI Learning Bayesian networks. Vibhav Gogate
Introduction to AI Learning Bayesian networks Vibhav Gogate Inductive Learning in a nutshell Given: Data Examples of a function (X, F(X)) Predict function F(X) for new examples X Discrete F(X): Classification
More informationBayesian Methods: Naïve Bayes
Bayesian Methods: aïve Bayes icholas 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 informationModern Information Retrieval
Modern Information Retrieval Chapter 8 Text Classification Introduction A Characterization of Text Classification Unsupervised Algorithms Supervised Algorithms Feature Selection or Dimensionality Reduction
More informationMidterm. You may use a calculator, but not any device that can access the Internet or store large amounts of data.
INST 737 April 1, 2013 Midterm Name: }{{} by writing my name I swear by the honor code Read all of the following information before starting the exam: For free response questions, show all work, clearly
More informationLoss Functions, Decision Theory, and Linear Models
Loss Functions, Decision Theory, and Linear Models CMSC 678 UMBC January 31 st, 2018 Some slides adapted from Hamed Pirsiavash Logistics Recap Piazza (ask & answer questions): https://piazza.com/umbc/spring2018/cmsc678
More informationINFO 4300 / CS4300 Information Retrieval. slides adapted from Hinrich Schütze s, linked from
INFO 4300 / CS4300 Information Retrieval slides adapted from Hinrich Schütze s, linked from http://informationretrieval.org/ IR 26/26: Feature Selection and Exam Overview Paul Ginsparg Cornell University,
More informationMIDTERM: CS 6375 INSTRUCTOR: VIBHAV GOGATE October,
MIDTERM: CS 6375 INSTRUCTOR: VIBHAV GOGATE October, 23 2013 The exam is closed book. You are allowed a one-page cheat sheet. Answer the questions in the spaces provided on the question sheets. If you run
More informationData Mining Part 4. Prediction
Data Mining Part 4. Prediction 4.3. Fall 2009 Instructor: Dr. Masoud Yaghini Outline Introduction Bayes Theorem Naïve References Introduction Bayesian classifiers A statistical classifiers Introduction
More informationOn Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality. Weiqiang Dong
On Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality Weiqiang Dong 1 The goal of the work presented here is to illustrate that classification error responds to error in the target probability estimates
More informationNaïve Bayes classification. p ij 11/15/16. Probability theory. Probability theory. Probability theory. X P (X = x i )=1 i. Marginal Probability
Probability theory Naïve Bayes classification Random variable: a variable whose possible values are numerical outcomes of a random phenomenon. s: A person s height, the outcome of a coin toss Distinguish
More informationGenerative Learning. INFO-4604, Applied Machine Learning University of Colorado Boulder. November 29, 2018 Prof. Michael Paul
Generative Learning INFO-4604, Applied Machine Learning University of Colorado Boulder November 29, 2018 Prof. Michael Paul Generative vs Discriminative The classification algorithms we have seen so far
More informationClassification: Naïve Bayes. Nathan Schneider (slides adapted from Chris Dyer, Noah Smith, et al.) ENLP 19 September 2016
Classification: Naïve Bayes Nathan Schneider (slides adapted from Chris Dyer, Noah Smith, et al.) ENLP 19 September 2016 1 Sentiment Analysis Recall the task: Filled with horrific dialogue, laughable characters,
More informationBrief Introduction to Machine Learning
Brief Introduction to Machine Learning Yuh-Jye Lee Lab of Data Science and Machine Intelligence Dept. of Applied Math. at NCTU August 29, 2016 1 / 49 1 Introduction 2 Binary Classification 3 Support Vector
More informationGenerative Clustering, Topic Modeling, & Bayesian Inference
Generative Clustering, Topic Modeling, & Bayesian Inference INFO-4604, Applied Machine Learning University of Colorado Boulder December 12-14, 2017 Prof. Michael Paul Unsupervised Naïve Bayes Last week
More informationDiscriminant Analysis and Statistical Pattern Recognition
Discriminant Analysis and Statistical Pattern Recognition GEOFFREY J. McLACHLAN Department of Mathematics The University of Queensland St. Lucia, Queensland, Australia A Wiley-Interscience Publication
More informationOutline. Supervised Learning. Hong Chang. Institute of Computing Technology, Chinese Academy of Sciences. Machine Learning Methods (Fall 2012)
Outline Hong Chang Institute of Computing Technology, Chinese Academy of Sciences Machine Learning Methods (Fall 2012) Outline Outline I 1 Linear Models for Regression Linear Regression Probabilistic Interpretation
More informationA Probabilistic Model for Online Document Clustering with Application to Novelty Detection
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection Jian Zhang School of Computer Science Cargenie Mellon University Pittsburgh, PA 15213 jian.zhang@cs.cmu.edu Zoubin
More informationInformation Retrieval (Text Categorization)
Information Retrieval (Text Categorization) Fabio Aiolli http://www.math.unipd.it/~aiolli Dipartimento di Matematica Università di Padova Anno Accademico 1 Text Categorization Text categorization (TC -
More informationWord Sense Disambiguation (WSD) Based on Foundations of Statistical NLP by C. Manning & H. Schütze, ch. 7 MIT Press, 2002
0. Word Sense Disambiguation (WSD) Based on Foundations of Statistical NLP by C. Manning & H. Schütze, ch. 7 MIT Press, 2002 WSD Examples 1. They have the right to bear arms. (drept) The sign on the right
More informationIntroduction to Machine Learning Midterm Exam Solutions
10-701 Introduction to Machine Learning Midterm Exam Solutions Instructors: Eric Xing, Ziv Bar-Joseph 17 November, 2015 There are 11 questions, for a total of 100 points. This exam is open book, open notes,
More informationIntroduction. Le Song. Machine Learning I CSE 6740, Fall 2013
Introduction Le Song Machine Learning I CSE 6740, Fall 2013 What is Machine Learning (ML) Study of algorithms that improve their performance at some task with experience 2 Common to Industrial scale problems
More informationCS6220: DATA MINING TECHNIQUES
CS6220: DATA MINING TECHNIQUES Matrix Data: Classification: Part 2 Instructor: Yizhou Sun yzsun@ccs.neu.edu September 21, 2014 Methods to Learn Matrix Data Set Data Sequence Data Time Series Graph & Network
More informationMidterm Review CS 6375: Machine Learning. Vibhav Gogate The University of Texas at Dallas
Midterm Review CS 6375: Machine Learning Vibhav Gogate The University of Texas at Dallas Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Parametric Y Continuous Non-parametric
More informationBayes Classifiers. CAP5610 Machine Learning Instructor: Guo-Jun QI
Bayes Classifiers CAP5610 Machine Learning Instructor: Guo-Jun QI Recap: Joint distributions Joint distribution over Input vector X = (X 1, X 2 ) X 1 =B or B (drinking beer or not) X 2 = H or H (headache
More information