SlideShare a Scribd company logo

powerpoint

•Download as PPT, PDF•
•
B
butest
1 of 48
CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 15: Learning to Rank
Machine learning for IR ranking? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sec. 15.4
 
Machine learning for IR ranking ,[object Object],[object Object],[object Object],[object Object]
Machine learning for IR ranking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why weren’t early attempts very successful/influential? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why wasn’t ML much needed? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why is ML needed now ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simple example: Using classification for ad hoc IR ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sec. 15.4.1
Simple example: Using classification for ad hoc IR ,[object Object],[object Object],[object Object],[object Object],[object Object],Sec. 15.4.1
Simple example: Using classification for ad hoc IR 0 2 3 4 5 0.05 0.025 cosine score  Term proximity  R R R R R R R R R R R N N N N N N N N N N Sec. 15.4.1 Decision surface
More complex example of using classification for search ranking [Nallapati 2004] ,[object Object],[object Object]
An SVM classifier for information retrieval [Nallapati 2004] ,[object Object],[object Object],[object Object],[object Object],[object Object]
An SVM classifier for information retrieval [Nallapati 2004] ,[object Object],[object Object],[object Object],[object Object],[object Object]
An SVM classifier for information retrieval [Nallapati 2004] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Train Test Disk 3 Disk 4-5 WT10G (web) Disk 3 LM 0.1785 0.2503 0.2666 SVM 0.1728 0.2432 0.2750 Disk 4-5 LM 0.1773 0.2516 0.2656 SVM 0.1646 0.2355 0.2675
“ Learning to rank” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sec. 15.4.2
“Learning to rank” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Point-wise learning ,[object Object]
The Ranking SVM [Herbrich et al. 1999, 2000; Joachims et al. 2002] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sec. 15.4.2
The Ranking SVM [Herbrich et al. 1999, 2000; Joachims et al. 2002] ,[object Object],Sec. 15.4.2
The Ranking SVM [Herbrich et al. 1999, 2000; Joachims et al. 2002] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sec. 15.4.2
The Ranking SVM [Herbrich et al. 1999, 2000; Joachims et al. 2002] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sec. 15.4.2
The SVM loss function ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The SVM loss function ,[object Object],[object Object],[object Object],Loss 1 w  Φ u Hinge loss Regularizer of ‖ w ‖
Adapting the Ranking SVM for (successful) Information Retrieval ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The ranking SVM fails to model the IR problem well… ,[object Object],[object Object],[object Object],[object Object]
Based on the LETOR test collection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Principal components projection of 2 queries [solid = q12, open = q50; circle = DR, square = PR, triangle = NR]
Ranking scale importance discrepancy [r3 = Definitely Relevant, r2 = Partially Relevant, r1 = Nonrelevant]
Number of training documents per query discrepancy [solid = q12, open = q50]
IR Evaluation Measures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Normalized Discounted Cumulative Gain (NDCG) evaluation measure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],rank r gain discount Sec. 8.4
Recap: Two Problems with Direct Application of the Ranking SVM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
These problems are solved with a new Loss function ,[object Object],[object Object],[object Object],[object Object]
Optimization (Gradient Descent)
Optimization (Quadratic Programming)
Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Results (OHSUMED)
MSN Search [now Bing] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Results (MSN search)
Alternative: Optimizing Rank-Based Measures [Yue et al. SIGIR 2007] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Discontinuity Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],NDCG = 0.63 NDCG discontinuous w.r.t model parameters! d 1 d 2 d 3 Retrieval Score 0.9 0.6 0.3 Rank 1 2 3 Relevance 0 1 0
Structural SVMs [Tsochantaridis et al., 2007] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Structural SVM training [Tsochantaridis et al., 2007] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Structural SVM training proceeds incrementally by starting with a working set of constraints, and adding in the most violated constraint at each iteration
Other machine learning methods for learning to rank ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Limitation of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object]
Resources ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Recommended

Chapter 7 ds
Chapter 7 dsChapter 7 ds
Chapter 7 dsHanif Durad
 
Frequency Estimation
Frequency EstimationFrequency Estimation
Frequency EstimationPeter Kootsookos
 
Chapter 10 ds
Chapter 10 dsChapter 10 ds
Chapter 10 dsHanif Durad
 
Adversarial Reinforced Learning for Unsupervised Domain Adaptation
Adversarial Reinforced Learning for Unsupervised Domain AdaptationAdversarial Reinforced Learning for Unsupervised Domain Adaptation
Adversarial Reinforced Learning for Unsupervised Domain Adaptationtaeseon ryu
 
Week 1 - Data Structures and Algorithms
Week 1 - Data Structures and AlgorithmsWeek 1 - Data Structures and Algorithms
Week 1 - Data Structures and AlgorithmsFerdin Joe John Joseph PhD
 
Dynamic Memory & Linked Lists
Dynamic Memory & Linked ListsDynamic Memory & Linked Lists
Dynamic Memory & Linked ListsAfaq Mansoor Khan
 
Data structures
Data structuresData structures
Data structuresSaurabh Mishra
 
Lect6 csp
Lect6 cspLect6 csp
Lect6 csptrivedidr
 

Recommended

Chapter 7 ds
Chapter 7 dsChapter 7 ds
Chapter 7 dsHanif Durad
 
Frequency Estimation
Frequency EstimationFrequency Estimation
Frequency EstimationPeter Kootsookos
 
Chapter 10 ds
Chapter 10 dsChapter 10 ds
Chapter 10 dsHanif Durad
 
Adversarial Reinforced Learning for Unsupervised Domain Adaptation
Adversarial Reinforced Learning for Unsupervised Domain AdaptationAdversarial Reinforced Learning for Unsupervised Domain Adaptation
Adversarial Reinforced Learning for Unsupervised Domain Adaptationtaeseon ryu
 
Week 1 - Data Structures and Algorithms
Week 1 - Data Structures and AlgorithmsWeek 1 - Data Structures and Algorithms
Week 1 - Data Structures and AlgorithmsFerdin Joe John Joseph PhD
 
Dynamic Memory & Linked Lists
Dynamic Memory & Linked ListsDynamic Memory & Linked Lists
Dynamic Memory & Linked ListsAfaq Mansoor Khan
 
Data structures
Data structuresData structures
Data structuresSaurabh Mishra
 
Lect6 csp
Lect6 cspLect6 csp
Lect6 csptrivedidr
 
Relational Calculus
Relational CalculusRelational Calculus
Relational CalculusDr. C.V. Suresh Babu
 
RBM from Scratch
RBM from Scratch RBM from Scratch
RBM from Scratch Hadi Sinaee
 
Text categorization as graph
Text categorization as graphText categorization as graph
Text categorization as graphHarry Potter
 
2.7 other classifiers
2.7 other classifiers2.7 other classifiers
2.7 other classifiersKrish_ver2
 
Data structure and algorithm notes
Data structure and algorithm notesData structure and algorithm notes
Data structure and algorithm notessuman khadka
 
Intro to Logistic Regression
Intro to Logistic RegressionIntro to Logistic Regression
Intro to Logistic RegressionJay Victoria
 
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...Spark Summit
 
Chapter 6 ds
Chapter 6 dsChapter 6 ds
Chapter 6 dsHanif Durad
 
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...Waqas Nawaz
 
Algorithm Design and Complexity - Course 1&2
Algorithm Design and Complexity - Course 1&2Algorithm Design and Complexity - Course 1&2
Algorithm Design and Complexity - Course 1&2Traian Rebedea
 
Hybrid acquisition of temporal scopes for rdf data
Hybrid acquisition of temporal scopes for rdf dataHybrid acquisition of temporal scopes for rdf data
Hybrid acquisition of temporal scopes for rdf dataAnisa Rula
 
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic Progamming
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic ProgammingAlgorithm Design and Complexity - Course 4 - Heaps and Dynamic Progamming
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic ProgammingTraian Rebedea
 
Data Structure and Algorithms
Data Structure and AlgorithmsData Structure and Algorithms
Data Structure and Algorithmsiqbalphy1
 
Neural network for machine learning
Neural network for machine learningNeural network for machine learning
Neural network for machine learningUjjawal
 
Data Structures and Algorithm - Week 4 - Trees, Binary Trees
Data Structures and Algorithm - Week 4 - Trees, Binary TreesData Structures and Algorithm - Week 4 - Trees, Binary Trees
Data Structures and Algorithm - Week 4 - Trees, Binary TreesFerdin Joe John Joseph PhD
 
Hadoop exercise
Hadoop exerciseHadoop exercise
Hadoop exerciseSubhas Kumar Ghosh
 
Ranking SVM
Ranking SVMRanking SVM
Ranking SVMhit_alex
 
Optimizing Search Engines using Clickthrough Data
Optimizing Search Engines using Clickthrough DataOptimizing Search Engines using Clickthrough Data
Optimizing Search Engines using Clickthrough DataKoji Yoshida
 
Machine Learned Relevance at A Large Scale Search Engine
Machine Learned Relevance at A Large Scale Search EngineMachine Learned Relevance at A Large Scale Search Engine
Machine Learned Relevance at A Large Scale Search EngineSalford Systems
 
Information Retrieval Evaluation
Information Retrieval EvaluationInformation Retrieval Evaluation
Information Retrieval EvaluationJosé Ramón Ríos Viqueira
 
Open Information Extraction 2nd
Open Information Extraction 2ndOpen Information Extraction 2nd
Open Information Extraction 2ndhit_alex
 
Social Network Analysis
Social Network AnalysisSocial Network Analysis
Social Network AnalysisGiorgos Cheliotis
 

More Related Content

What's hot

RBM from Scratch
RBM from Scratch RBM from Scratch
RBM from Scratch Hadi Sinaee
 
Text categorization as graph
Text categorization as graphText categorization as graph
Text categorization as graphHarry Potter
 
2.7 other classifiers
2.7 other classifiers2.7 other classifiers
2.7 other classifiersKrish_ver2
 
Data structure and algorithm notes
Data structure and algorithm notesData structure and algorithm notes
Data structure and algorithm notessuman khadka
 
Intro to Logistic Regression
Intro to Logistic RegressionIntro to Logistic Regression
Intro to Logistic RegressionJay Victoria
 
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...Spark Summit
 
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...Waqas Nawaz
 
Algorithm Design and Complexity - Course 1&2
Algorithm Design and Complexity - Course 1&2Algorithm Design and Complexity - Course 1&2
Algorithm Design and Complexity - Course 1&2Traian Rebedea
 
Hybrid acquisition of temporal scopes for rdf data
Hybrid acquisition of temporal scopes for rdf dataHybrid acquisition of temporal scopes for rdf data
Hybrid acquisition of temporal scopes for rdf dataAnisa Rula
 
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic Progamming
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic ProgammingAlgorithm Design and Complexity - Course 4 - Heaps and Dynamic Progamming
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic ProgammingTraian Rebedea
 
Data Structure and Algorithms
Data Structure and AlgorithmsData Structure and Algorithms
Data Structure and Algorithmsiqbalphy1
 
Neural network for machine learning
Neural network for machine learningNeural network for machine learning
Neural network for machine learningUjjawal
 
Data Structures and Algorithm - Week 4 - Trees, Binary Trees
Data Structures and Algorithm - Week 4 - Trees, Binary TreesData Structures and Algorithm - Week 4 - Trees, Binary Trees
Data Structures and Algorithm - Week 4 - Trees, Binary TreesFerdin Joe John Joseph PhD
 

What's hot (16)

Relational Calculus
Relational CalculusRelational Calculus
Relational Calculus
 
RBM from Scratch
RBM from Scratch RBM from Scratch
RBM from Scratch
 
Text categorization as graph
Text categorization as graphText categorization as graph
Text categorization as graph
 
2.7 other classifiers
2.7 other classifiers2.7 other classifiers
2.7 other classifiers
 
Data structure and algorithm notes
Data structure and algorithm notesData structure and algorithm notes
Data structure and algorithm notes
 
Intro to Logistic Regression
Intro to Logistic RegressionIntro to Logistic Regression
Intro to Logistic Regression
 
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...
Netflix's Recommendation ML Pipeline Using Apache Spark: Spark Summit East ta...
 
Chapter 6 ds
Chapter 6 dsChapter 6 ds
Chapter 6 ds
 
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...
ICDE-2015 Shortest Path Traversal Optimization and Analysis for Large Graph C...
 
Algorithm Design and Complexity - Course 1&2
Algorithm Design and Complexity - Course 1&2Algorithm Design and Complexity - Course 1&2
Algorithm Design and Complexity - Course 1&2
 
Hybrid acquisition of temporal scopes for rdf data
Hybrid acquisition of temporal scopes for rdf dataHybrid acquisition of temporal scopes for rdf data
Hybrid acquisition of temporal scopes for rdf data
 
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic Progamming
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic ProgammingAlgorithm Design and Complexity - Course 4 - Heaps and Dynamic Progamming
Algorithm Design and Complexity - Course 4 - Heaps and Dynamic Progamming
 
Data Structure and Algorithms
Data Structure and AlgorithmsData Structure and Algorithms
Data Structure and Algorithms
 
Neural network for machine learning
Neural network for machine learningNeural network for machine learning
Neural network for machine learning
 
Data Structures and Algorithm - Week 4 - Trees, Binary Trees
Data Structures and Algorithm - Week 4 - Trees, Binary TreesData Structures and Algorithm - Week 4 - Trees, Binary Trees
Data Structures and Algorithm - Week 4 - Trees, Binary Trees
 
Hadoop exercise
Hadoop exerciseHadoop exercise
Hadoop exercise
 

Viewers also liked

Ranking SVM
Ranking SVMRanking SVM
Ranking SVMhit_alex
 
Optimizing Search Engines using Clickthrough Data
Optimizing Search Engines using Clickthrough DataOptimizing Search Engines using Clickthrough Data
Optimizing Search Engines using Clickthrough DataKoji Yoshida
 
Machine Learned Relevance at A Large Scale Search Engine
Machine Learned Relevance at A Large Scale Search EngineMachine Learned Relevance at A Large Scale Search Engine
Machine Learned Relevance at A Large Scale Search EngineSalford Systems
 
Open Information Extraction 2nd
Open Information Extraction 2ndOpen Information Extraction 2nd
Open Information Extraction 2ndhit_alex
 

Viewers also liked (6)

Ranking SVM
Ranking SVMRanking SVM
Ranking SVM
 
Optimizing Search Engines using Clickthrough Data
Optimizing Search Engines using Clickthrough DataOptimizing Search Engines using Clickthrough Data
Optimizing Search Engines using Clickthrough Data
 
Machine Learned Relevance at A Large Scale Search Engine
Machine Learned Relevance at A Large Scale Search EngineMachine Learned Relevance at A Large Scale Search Engine
Machine Learned Relevance at A Large Scale Search Engine
 
Information Retrieval Evaluation
Information Retrieval EvaluationInformation Retrieval Evaluation
Information Retrieval Evaluation
 
Open Information Extraction 2nd
Open Information Extraction 2ndOpen Information Extraction 2nd
Open Information Extraction 2nd
 
Social Network Analysis
Social Network AnalysisSocial Network Analysis
Social Network Analysis
 

Similar to powerpoint

lecture14-learning-ranking.ppt
lecture14-learning-ranking.pptlecture14-learning-ranking.ppt
lecture14-learning-ranking.pptVishalKumar725248
 
lecture14-learning-ranking.ppt
lecture14-learning-ranking.pptlecture14-learning-ranking.ppt
lecture14-learning-ranking.pptVishalKumar725248
 
Part 1
Part 1Part 1
Part 1butest
 
probabilistic ranking
probabilistic rankingprobabilistic ranking
probabilistic rankingFELIX75
 
report.doc
report.docreport.doc
report.docbutest
 
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docxDIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docxlynettearnold46882
 
Keynote at IWLS 2017
Keynote at IWLS 2017Keynote at IWLS 2017
Keynote at IWLS 2017Manish Pandey
 
Machine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.pptMachine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.pptAnshika865276
 
Data Structures- Part1 overview and review
Data Structures- Part1 overview and reviewData Structures- Part1 overview and review
Data Structures- Part1 overview and reviewAbdullah Al-hazmy
 
Analytics Boot Camp - Slides
Analytics Boot Camp - SlidesAnalytics Boot Camp - Slides
Analytics Boot Camp - SlidesAditya Joshi
 
Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnBenjamin Bengfort
 
vorl1.ppt
vorl1.pptvorl1.ppt
vorl1.pptbutest
 
And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018
And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018
And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018Codemotion
 
Software Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and MetricsSoftware Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and MetricsProgrameter
 
Recommender Systems from A to Z – Model Training
Recommender Systems from A to Z – Model TrainingRecommender Systems from A to Z – Model Training
Recommender Systems from A to Z – Model TrainingCrossing Minds
 
The ABC of Implementing Supervised Machine Learning with Python.pptx
The ABC of Implementing Supervised Machine Learning with Python.pptxThe ABC of Implementing Supervised Machine Learning with Python.pptx
The ABC of Implementing Supervised Machine Learning with Python.pptxRuby Shrestha
 

Similar to powerpoint (20)

lecture14-learning-ranking.ppt
lecture14-learning-ranking.pptlecture14-learning-ranking.ppt
lecture14-learning-ranking.ppt
 
lecture14-learning-ranking.ppt
lecture14-learning-ranking.pptlecture14-learning-ranking.ppt
lecture14-learning-ranking.ppt
 
Part 1
Part 1Part 1
Part 1
 
probabilistic ranking
probabilistic rankingprobabilistic ranking
probabilistic ranking
 
report.doc
report.docreport.doc
report.doc
 
ML_Lecture_1.ppt
ML_Lecture_1.pptML_Lecture_1.ppt
ML_Lecture_1.ppt
 
CSC410-Presentation
CSC410-PresentationCSC410-Presentation
CSC410-Presentation
 
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docxDIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docx
 
Keynote at IWLS 2017
Keynote at IWLS 2017Keynote at IWLS 2017
Keynote at IWLS 2017
 
Machine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.pptMachine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.ppt
 
Data Structures- Part1 overview and review
Data Structures- Part1 overview and reviewData Structures- Part1 overview and review
Data Structures- Part1 overview and review
 
Introduction to r
Introduction to rIntroduction to r
Introduction to r
 
Analytics Boot Camp - Slides
Analytics Boot Camp - SlidesAnalytics Boot Camp - Slides
Analytics Boot Camp - Slides
 
Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-Learn
 
vorl1.ppt
vorl1.pptvorl1.ppt
vorl1.ppt
 
And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018
And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018
And Then There Are Algorithms - Danilo Poccia - Codemotion Rome 2018
 
nnml.ppt
nnml.pptnnml.ppt
nnml.ppt
 
Software Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and MetricsSoftware Measurement: Lecture 1. Measures and Metrics
Software Measurement: Lecture 1. Measures and Metrics
 
Recommender Systems from A to Z – Model Training
Recommender Systems from A to Z – Model TrainingRecommender Systems from A to Z – Model Training
Recommender Systems from A to Z – Model Training
 
The ABC of Implementing Supervised Machine Learning with Python.pptx
The ABC of Implementing Supervised Machine Learning with Python.pptxThe ABC of Implementing Supervised Machine Learning with Python.pptx
The ABC of Implementing Supervised Machine Learning with Python.pptx
 

More from butest

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEbutest
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jacksonbutest
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer IIbutest
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.docbutest
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1butest
 
Facebook
Facebook Facebook
Facebook butest
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...butest
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTbutest
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docbutest
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docbutest
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.docbutest
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!butest
 

More from butest (20)

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jackson
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer II
 
PPT
PPTPPT
PPT
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.doc
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1
 
Facebook
Facebook Facebook
Facebook
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENT
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.doc
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.doc
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.doc
 
hier
hierhier
hier
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!
 

powerpoint

Editor's Notes

  1. Rank r2 and r1 documents dominate, so RSVM is close to them, not the crucial r3-r2 distinction
  2. There are many more training documents for q50 than q12, so it dominates where the normal vector is drawn.
  3. Note how number of pairs blows out versus size of scored result set.
  4. References: I. Tsochantaridis, T. Joachims, T. Hofmann, Y. Altun. “Large Margin Methods for Structured and Interdependent Output Variables.” Journal of Machine Learning Research, 6, 1453-1484, 2005.