Information Retrieval and Organisation
|
|
|
- Gertrude Winifred Harris
- 5 years ago
- Views:
Transcription
1 Information Retrieval and Organisation Chapter 13 Text Classification and Naïve Bayes Dell Zhang Birkbeck, University of London
2 Motivation Relevance Feedback revisited The user marks a number of documents as relevant/nonrelevant We then try to use this information to return better search results. Suppose we just tried to learn a filter for nonrelevant documents This is an instance of a text classification problem: Two classes : relevant, nonrelevant For each document, decide whether it is relevant or nonrelevant
3 Motivation The path from information retrieval to text classification: You have an information need, say: Unrest in the Niger delta region You want to rerun an appropriate query periodically to find new news items on this topic You will be sent new documents that are found I.e., it s classification not ranking Such queries are called standing queries Long used by information professionals A modern mass instantiation is
4
5 Motivation Many search engine functionalities use classification The notion of classification is very general and has many applications within and beyond IR
6 Text Classification/Categorization Given: A document, dd. A set of classes C = {c 1, c 2,, c n }. Determine: The class of d: c(dc, where c(d is a classification function ( classifier.
7 Text Classification Examples Classes are most often topics such as Yahoocategories e.g., finance, sports, news>world>asia>business Classes may be genres e.g., editorials, movie-reviews, news Classes may be opinion on a person/product e.g., like, hate, neutral
8 Text Classification Examples Classes may be domain-specific e.g., interesting-to-me vs. not-interesting-to-me e.g., contains-adult-language vs. doesn t e.g., English, French, Chinese, (language identification e.g., about-linux vs not-about-linux (vertical search e.g., link-spam vs. not-link-spam
9 Classification Methods (1 Manual Classification Used by Yahoo! (originally; now present but downplayed, Looksmart, about.com, ODP, PubMed, Very accurate when job is done by experts Consistent when the problem size and team is small Difficult and expensive to scale We need automatic classification methods for big problems.
10 Classification Methods (2 Hand-Coded Rules Used by CIA, Reuters, CS dept s spam filter, Commercial systems for standing queries have complex query languages (everything in IR query languages plus accumulators Accuracy is often quite high, if the rules have been carefully refined over time by experts. Expensive to build/maintain the rules.
11 Companies (such as Verity provide IDE for writing such complex classification rules Hand-weighting of terms Maintenance issues (author, etc.
12 Classification Methods (3 (Supervised Machine Learning Used by Google, Yahoo!, MSN, Autonomy, Verity, Enkata, Note that many commercial systems use a mixture of methods There is no free lunch: hand-classified training data are required. But the training data can be built up (and refined easily by amateurs. Such as graduate students
13 Text Classification via ML Learning Predicting L Classifier U Training Documents Test Documents
14 Text Classification via ML Test Data: planning language proof intelligence Classes: ML (AI (Programming (HCI Planning Semantics Garb.Coll. Multimedia GUI Training Data: learning intelligence algorithm reinforcement network... planning temporal reasoning plan language... programming semantics language proof... garbage collection memory optimization region (Note: in real life there is often a hierarchy, not present in the above problem statement; and also, you may get multi-topic papers for example on ML approaches to Garb. Coll.
15 Evaluating Classification Classification Accuracy (#correct / #total The proportion of correct predictions Adequate if one class per document Precision, Recall F 1 measure (for each class Macro-averaging: computes performance measure for each class, and then computes a simple average over classes. Micro-averaging: pools per-document predictions across classes, and then computes performance measure on the pooled contingency table.
16 Evaluating Classification macro-averaged precision is [10/( /( ]/2 = ( /2 = 0.7 micro-averaged precision is 100/(
17 Evaluating Classification Evaluation must be done on test data that are independent of the training data (usually a disjoint set of instances. Results can vary based on sampling error due to different training and test sets. Average results over multiple training and test sets (splits of the overall data for the best results. Reuters-21578
18 Learning Curve Yahoo Science Data
19 Naïve Bayes Before seeing the content of document d Classify d to the class with maximum prior probability P(c. After seeing the content of document d Classify d to the class with maximum posteriori probability P(c d. For each class c j C, P(c j d can be estimated using the Bayes Rule.
20 Naïve Bayes Bayes Rule, Again! ( ( ( ( d P c P c d P d c P ( ( ( (, ( c P c d P d P d c P d c P
21 Naïve Bayes ( argmax ( d c P d c j c j C ( ( ( argmax d P c P c d P j j c j C ( ( argmax j j C c c P c d P j How can we estimate?
22 Naïve Bayes For each class c j C, P(c j can be estimated from the frequency of classes in the training data. P ( c j N j j N j where N j : the number of documents in the class c j
23 Naïve Bayes P(d c j = P(t 1, t 2,,t n c j There are O( X n C parameters. Could only be estimated if a very, very large number of training examples was available. To facilitate the estimation of P(d c j, two simplifying assumptions are made. Conditional Independence Assumption The term occurrences are independent of each other given the class. Positional Independence Assumption The conditional probabilities for a term are the same independent of position in the document.
24 Naïve Bayes Multinomial NB: effectively, the probability of each doc P(d c j is given by a class-specific unigram language model. c t 1 t 2 t 3 t 4 t 5 t 6 P ( d c P ( t c j t d i i j
25 Smoothing for NB Why not just use MLE? If a term t (in a test doc d did not occur in the training data, P(t c j would be 0, and then P(d c j would be 0 no matter how strongly other terms in d are associated with class c j. Add-One (Laplace Smoothing P ( t c i j T i ji T ji P ( t i c j T i ji T ji 1 1 T ji : the number of occurrences of term i in documents of class c j
26 Underflow Prevention Multiplying lots of probabilities, which are between 0 and 1 by definition, can result in floating-point underflow. Since log(xy = log(x + log(y, it is better to perform all computations by summing logs of probabilities rather than multiplying probabilities. Class with highest final un-normalized log probability score is still the most probable. c NB argmax c C j log P ( c j i positions log P ( t Note that the model is now just max of sum of weights i c j
27 NB Algorithm: Training
28 NB Algorithm: Testing
29 Time Complexity Training Time: O( D L d + C V where L d is the average length of a document in D. Assumes V and all D i, n i, and n ij pre-computed in O( D L d time during one pass through all of the data. Generally just O( D L d since usually C V < D L d Testing Time: O( C L t where L t is the average length of a test document. Why? Very efficient overall, linearly proportional to the time needed to just read in all the data.
30 Naïve Bayes is Not So Naïve Effectiveness The Bayes optimal classifier if the independence assumptions do hold. Often performs well even if the independence assumptions are badly violated. Robust to irrelevant features. Good in domains with many equally important features. A good dependable baseline for text classification (though may not be the best.
31 Naïve Bayes is Not So Naïve Efficiency Very fast Linear training/testing time complexity One pass of counting over the data Low storage requirements.
32 Application: Web Page Cat. WebKB Experiment (1998 Classify webpages from CS departments into: student, faculty, course,project Train on ~5,000 hand-labeled web pages Cornell, Washington, U.Texas, Wisconsin Crawl and classify a new site (CMU Student Faculty Person Project Course Departmt Extracted Correct Accuracy: 72% 42% 79% 73% 89% 100%
33 Application: Filtering Naïve Bayes has found a home in spam filtering Paul Graham s A Plan for Spam A mutant with more mutant offspring... Naive Bayes-like classifier with weird parameter estimation Widely used in spam filters Classic Naive Bayes superior when appropriately used (According to David D. Lewis But also many other things: black hole lists, etc. Many topic filters also use NB classifiers and Related
34 Application: Direct Marketing KDD-CUP 97 competition Task: to predict if the recipient of mail will actually respond to the advertisement Financial services industry 750,000 records Naïve Bayes: the 1 st & 2 nd place in among 16 (then state-of-the-art algorithms.
Part 9: Text Classification; The Naïve Bayes algorithm Francesco Ricci
Part 9: Text Classification; The Naïve Bayes algorithm Francesco Ricci Most of these slides comes from the course: Information Retrieval and Web Search, Christopher Manning and Prabhakar Raghavan 1 Content
naive 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
Bayes 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)
Text 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
Probability 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
Text 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)
Text 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)
Categorization 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
ANLP 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
Text 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
Behavioral Data Mining. Lecture 2
Behavioral Data Mining Lecture 2 Autonomy Corp Bayes Theorem Bayes Theorem P(A B) = probability of A given that B is true. P(A B) = P(B A)P(A) P(B) In practice we are most interested in dealing with events
INFO 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,
Generative 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
Classification, 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
Introduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 13: Text Classification & Naive Bayes Hinrich Schütze Center for Information and Language Processing, University of Munich 2014-05-15
Today. Statistical Learning. Coin Flip. Coin Flip. Experiment 1: Heads. Experiment 1: Heads. Which coin will I use? Which coin will I use?
Today Statistical Learning Parameter Estimation: Maximum Likelihood (ML) Maximum A Posteriori (MAP) Bayesian Continuous case Learning Parameters for a Bayesian Network Naive Bayes Maximum Likelihood estimates
Information Retrieval
Introduction to Information Retrieval Lecture 11: Probabilistic Information Retrieval 1 Outline Basic Probability Theory Probability Ranking Principle Extensions 2 Basic Probability Theory For events A
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
Ranked Retrieval (2)
Text Technologies for Data Science INFR11145 Ranked Retrieval (2) Instructor: Walid Magdy 31-Oct-2017 Lecture Objectives Learn about Probabilistic models BM25 Learn about LM for IR 2 1 Recall: VSM & TFIDF
Introduction to NLP. Text Classification
NLP Introduction to NLP Text Classification Classification Assigning documents to predefined categories topics, languages, users A given set of classes C Given x, determine its class in C Hierarchical
Automatic Clasification: Naïve Bayes
Automatic Clasification: Naïve Bayes Wm&R a.a. 2012/13 R. Basili (slides borrowed by: H. Schutze Dipartimento di Informatica Sistemi e produzione Università di Roma Tor Vergata Email: basili@info.uniroma2.it
Naïve 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
Tackling the Poor Assumptions of Naive Bayes Text Classifiers
Tackling the Poor Assumptions of Naive Bayes Text Classifiers Jason Rennie MIT Computer Science and Artificial Intelligence Laboratory jrennie@ai.mit.edu Joint work with Lawrence Shih, Jaime Teevan and
Classification & 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
Modern Information Retrieval
Modern Information Retrieval Chapter 8 Text Classification Introduction A Characterization of Text Classification Unsupervised Algorithms Supervised Algorithms Feature Selection or Dimensionality Reduction
IR Models: The Probabilistic Model. Lecture 8
IR Models: The Probabilistic Model Lecture 8 ' * ) ( % $ $ +#! "#! '& & Probability of Relevance? ' ', IR is an uncertain process Information need to query Documents to index terms Query terms and index
A 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,
The Naïve Bayes Classifier. Machine Learning Fall 2017
The Naïve Bayes Classifier Machine Learning Fall 2017 1 Today s lecture The naïve Bayes Classifier Learning the naïve Bayes Classifier Practical concerns 2 Today s lecture The naïve Bayes Classifier Learning
Naïve Bayes, Maxent and Neural Models
Naïve Bayes, Maxent and Neural Models CMSC 473/673 UMBC Some slides adapted from 3SLP Outline Recap: classification (MAP vs. noisy channel) & evaluation Naïve Bayes (NB) classification Terminology: bag-of-words
Applied Natural Language Processing
Applied Natural Language Processing Info 256 Lecture 5: Text classification (Feb 5, 2019) David Bamman, UC Berkeley Data Classification A mapping h from input data x (drawn from instance space X) to a
Machine 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
CPSC 340: Machine Learning and Data Mining. MLE and MAP Fall 2017
CPSC 340: Machine Learning and Data Mining MLE and MAP Fall 2017 Assignment 3: Admin 1 late day to hand in tonight, 2 late days for Wednesday. Assignment 4: Due Friday of next week. Last Time: Multi-Class
Fast Logistic Regression for Text Categorization with Variable-Length N-grams
Fast Logistic Regression for Text Categorization with Variable-Length N-grams Georgiana Ifrim *, Gökhan Bakır +, Gerhard Weikum * * Max-Planck Institute for Informatics Saarbrücken, Germany + Google Switzerland
DATA 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
9/12/17. 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
Machine 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
Estimating 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
Generative 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
CSE446: 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
Naïve Bayes Classifiers
Naïve Bayes Classifiers Example: PlayTennis (6.9.1) Given a new instance, e.g. (Outlook = sunny, Temperature = cool, Humidity = high, Wind = strong ), we want to compute the most likely hypothesis: v NB
Ontology-Based News Recommendation
Ontology-Based News Recommendation Wouter IJntema Frank Goossen Flavius Frasincar Frederik Hogenboom Erasmus University Rotterdam, the Netherlands frasincar@ese.eur.nl Outline Introduction Hermes: News
CS 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
Statistical Ranking Problem
Statistical Ranking Problem Tong Zhang Statistics Department, Rutgers University Ranking Problems Rank a set of items and display to users in corresponding order. Two issues: performance on top and dealing
Classification: 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,
The 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
Machine Learning
Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 26, 2015 Today: Bayes Classifiers Conditional Independence Naïve Bayes Readings: Mitchell: Naïve Bayes
Classification: Analyzing Sentiment
Classification: Analyzing Sentiment STAT/CSE 416: Machine Learning Emily Fox University of Washington April 17, 2018 Predicting sentiment by topic: An intelligent restaurant review system 1 4/16/18 It
Text 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
RETRIEVAL MODELS. Dr. Gjergji Kasneci Introduction to Information Retrieval WS
RETRIEVAL MODELS Dr. Gjergji Kasneci Introduction to Information Retrieval WS 2012-13 1 Outline Intro Basics of probability and information theory Retrieval models Boolean model Vector space model Probabilistic
PROBABILISTIC LATENT SEMANTIC ANALYSIS
PROBABILISTIC LATENT SEMANTIC ANALYSIS Lingjia Deng Revised from slides of Shuguang Wang Outline Review of previous notes PCA/SVD HITS Latent Semantic Analysis Probabilistic Latent Semantic Analysis Applications
PV211: Introduction to Information Retrieval
PV211: Introduction to Information Retrieval http://www.fi.muni.cz/~sojka/pv211 IIR 11: Probabilistic Information Retrieval Handout version Petr Sojka, Hinrich Schütze et al. Faculty of Informatics, Masaryk
Classification 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:
Classification: Analyzing Sentiment
Classification: Analyzing Sentiment STAT/CSE 416: Machine Learning Emily Fox University of Washington April 17, 2018 Predicting sentiment by topic: An intelligent restaurant review system 1 It s a big
Information Retrieval
Introduction to Information Retrieval Lecture 12: Language Models for IR Outline Language models Language Models for IR Discussion What is a language model? We can view a finite state automaton as a deterministic
1 Information retrieval fundamentals
CS 630 Lecture 1: 01/26/2006 Lecturer: Lillian Lee Scribes: Asif-ul Haque, Benyah Shaparenko This lecture focuses on the following topics Information retrieval fundamentals Vector Space Model (VSM) Deriving
University of Illinois at Urbana-Champaign. Midterm Examination
University of Illinois at Urbana-Champaign Midterm Examination CS410 Introduction to Text Information Systems Professor ChengXiang Zhai TA: Azadeh Shakery Time: 2:00 3:15pm, Mar. 14, 2007 Place: Room 1105,
Topics. Bayesian Learning. What is Bayesian Learning? Objectives for Bayesian Learning
Topics Bayesian Learning Sattiraju Prabhakar CS898O: ML Wichita State University Objectives for Bayesian Learning Bayes Theorem and MAP Bayes Optimal Classifier Naïve Bayes Classifier An Example Classifying
Lecture 13: More uses of Language Models
Lecture 13: More uses of Language Models William Webber (william@williamwebber.com) COMP90042, 2014, Semester 1, Lecture 13 What we ll learn in this lecture Comparing documents, corpora using LM approaches
Loss 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
Ad Placement Strategies
Case Study 1: Estimating Click Probabilities Tackling an Unknown Number of Features with Sketching Machine Learning for Big Data CSE547/STAT548, University of Washington Emily Fox 2014 Emily Fox January
Bayesian 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
Naive Bayesian Text Classifier Based on Different Probability Model
aive Bayesian Text Classifier Based on Different Probability Model,,3 LV Pin, Zhong Luo College of Computer Science and Technology, Wuhan University of Technology, Wuhan,China, lpwhict@63.com,zhongluo.netease.com
Preliminaries. Data Mining. The art of extracting knowledge from large bodies of structured data. Let s put it to use!
Data Mining The art of extracting knowledge from large bodies of structured data. Let s put it to use! 1 Recommendations 2 Basic Recommendations with Collaborative Filtering Making Recommendations 4 The
Introduction 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
Naïve Bayes classification
Naïve Bayes classification 1 Probability theory Random variable: a variable whose possible values are numerical outcomes of a random phenomenon. Examples: A person s height, the outcome of a coin toss
Generative Learning algorithms
CS9 Lecture notes Andrew Ng Part IV Generative Learning algorithms So far, we ve mainly been talking about learning algorithms that model p(y x; θ), the conditional distribution of y given x. For instance,
CS 188: Artificial Intelligence. Outline
CS 188: Artificial Intelligence Lecture 21: Perceptrons Pieter Abbeel UC Berkeley Many slides adapted from Dan Klein. Outline Generative vs. Discriminative Binary Linear Classifiers Perceptron Multi-class
Probabilistic Classification
Bayesian Networks Probabilistic Classification Goal: Gather Labeled Training Data Build/Learn a Probability Model Use the model to infer class labels for unlabeled data points Example: Spam Filtering...
Information Retrieval. Lecture 10: Text Classification;
Introduction ti to Information Retrieval Lecture 10: Tet Classification; The Naïve Bayes algorithm Relevance feedback revisited In relevance feedback, the user marks a number of documents as relevant/nonrelevant.
CPSC 340: Machine Learning and Data Mining
CPSC 340: Machine Learning and Data Mining MLE and MAP Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. 1 Admin Assignment 4: Due tonight. Assignment 5: Will be released
10/15/2015 A FAST REVIEW OF DISCRETE PROBABILITY (PART 2) Probability, Conditional Probability & Bayes Rule. Discrete random variables
Probability, Conditional Probability & Bayes Rule A FAST REVIEW OF DISCRETE PROBABILITY (PART 2) 2 Discrete random variables A random variable can take on one of a set of different values, each with an
CS 188: Artificial Intelligence Spring Announcements
CS 188: Artificial Intelligence Spring 2010 Lecture 22: Nearest Neighbors, Kernels 4/18/2011 Pieter Abbeel UC Berkeley Slides adapted from Dan Klein Announcements On-going: contest (optional and FUN!)
Hybrid Models for Text and Graphs. 10/23/2012 Analysis of Social Media
Hybrid Models for Text and Graphs 10/23/2012 Analysis of Social Media Newswire Text Formal Primary purpose: Inform typical reader about recent events Broad audience: Explicitly establish shared context
MIDTERM: 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
Modern Information Retrieval
Modern Information Retrieval Chapter 8 Text Classification Introduction A Characterization of Text Classification Unsupervised Algorithms Supervised Algorithms Feature Selection or Dimensionality Reduction
Midterm Examination Practice
University of Illinois at Urbana-Champaign Midterm Examination Practice CS598CXZ Advanced Topics in Information Retrieval (Fall 2013) Professor ChengXiang Zhai 1. Basic IR evaluation measures: The following
Latent Semantic Analysis. Hongning Wang
Latent Semantic Analysis Hongning Wang CS@UVa VS model in practice Document and query are represented by term vectors Terms are not necessarily orthogonal to each other Synonymy: car v.s. automobile Polysemy:
Latent Semantic Analysis. Hongning Wang
Latent Semantic Analysis Hongning Wang CS@UVa Recap: vector space model Represent both doc and query by concept vectors Each concept defines one dimension K concepts define a high-dimensional space Element
Data Mining. Practical Machine Learning Tools and Techniques. Slides for Chapter 4 of Data Mining by I. H. Witten, E. Frank and M. A.
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter of Data Mining by I. H. Witten, E. Frank and M. A. Hall Statistical modeling Opposite of R: use all the attributes Two assumptions:
Topic Models and Applications to Short Documents
Topic Models and Applications to Short Documents Dieu-Thu Le Email: dieuthu.le@unitn.it Trento University April 6, 2011 1 / 43 Outline Introduction Latent Dirichlet Allocation Gibbs Sampling Short Text
CS246 Final Exam, Winter 2011
CS246 Final Exam, Winter 2011 1. Your name and student ID. Name:... Student ID:... 2. I agree to comply with Stanford Honor Code. Signature:... 3. There should be 17 numbered pages in this exam (including
Bayesian Classifiers, Conditional Independence and Naïve Bayes. Required reading: Naïve Bayes and Logistic Regression (available on class website)
Bayesian Classifiers, Conditional Independence and Naïve Bayes Required reading: Naïve Bayes and Logistic Regression (available on class website) Machine Learning 10-701 Tom M. Mitchell Machine Learning
Naïve 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
CS 6375 Machine Learning
CS 6375 Machine Learning Nicholas Ruozzi University of Texas at Dallas Slides adapted from David Sontag and Vibhav Gogate Course Info. Instructor: Nicholas Ruozzi Office: ECSS 3.409 Office hours: Tues.
Introduction to Logistic Regression
Introduction to Logistic Regression Guy Lebanon Binary Classification Binary classification is the most basic task in machine learning, and yet the most frequent. Binary classifiers often serve as the
CSE 473: Artificial Intelligence Autumn Topics
CSE 473: Artificial Intelligence Autumn 2014 Bayesian Networks Learning II Dan Weld Slides adapted from Jack Breese, Dan Klein, Daphne Koller, Stuart Russell, Andrew Moore & Luke Zettlemoyer 1 473 Topics
CS4705. Probability Review and Naïve Bayes. Slides from Dragomir Radev
CS4705 Probability Review and Naïve Bayes Slides from Dragomir Radev Classification using a Generative Approach Previously on NLP discriminative models P C D here is a line with all the social media posts
Prediction of Citations for Academic Papers
000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050
Generative 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
Naïve 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:
Classifying 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,
Information Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 4: Probabilistic Retrieval Models April 29, 2010 Wolf-Tilo Balke and Joachim Selke Institut für Informationssysteme Technische Universität Braunschweig
Cross-Lingual Language Modeling for Automatic Speech Recogntion
GBO Presentation Cross-Lingual Language Modeling for Automatic Speech Recogntion November 14, 2003 Woosung Kim woosung@cs.jhu.edu Center for Language and Speech Processing Dept. of Computer Science The
Thanks to Jure Leskovec, Stanford and Panayiotis Tsaparas, Univ. of Ioannina for slides
Thanks to Jure Leskovec, Stanford and Panayiotis Tsaparas, Univ. of Ioannina for slides Web Search: How to Organize the Web? Ranking Nodes on Graphs Hubs and Authorities PageRank How to Solve PageRank
Understanding Comments Submitted to FCC on Net Neutrality. Kevin (Junhui) Mao, Jing Xia, Dennis (Woncheol) Jeong December 12, 2014
Understanding Comments Submitted to FCC on Net Neutrality Kevin (Junhui) Mao, Jing Xia, Dennis (Woncheol) Jeong December 12, 2014 Abstract We aim to understand and summarize themes in the 1.65 million
Soft Computing. Lecture Notes on Machine Learning. Matteo Mattecci.
Soft Computing Lecture Notes on Machine Learning Matteo Mattecci matteucci@elet.polimi.it Department of Electronics and Information Politecnico di Milano Matteo Matteucci c Lecture Notes on Machine Learning
Probability models for machine learning. Advanced topics ML4bio 2016 Alan Moses
Probability models for machine learning Advanced topics ML4bio 2016 Alan Moses What did we cover in this course so far? 4 major areas of machine learning: Clustering Dimensionality reduction Classification
Generative Classifiers: Part 1. CSC411/2515: Machine Learning and Data Mining, Winter 2018 Michael Guerzhoy and Lisa Zhang
Generative Classifiers: Part 1 CSC411/2515: Machine Learning and Data Mining, Winter 2018 Michael Guerzhoy and Lisa Zhang 1 This Week Discriminative vs Generative Models Simple Model: Does the patient
A Neural Passage Model for Ad-hoc Document Retrieval
A Neural Passage Model for Ad-hoc Document Retrieval Qingyao Ai, Brendan O Connor, and W. Bruce Croft College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA,
CS276A Text Information Retrieval, Mining, and Exploitation. Lecture 4 15 Oct 2002
CS276A Text Information Retrieval, Mining, and Exploitation Lecture 4 15 Oct 2002 Recap of last time Index size Index construction techniques Dynamic indices Real world considerations 2 Back of the envelope