<--- Back to Details
First PageDocument Content
Ranking function / Ranking SVM / Relevance feedback / Google Search / Web search query / Search engine / Document retrieval / Bing / Web query classification / Information science / Information retrieval / Learning to rank
Date: 2011-02-25 12:41:31
Ranking function
Ranking SVM
Relevance feedback
Google Search
Web search query
Search engine
Document retrieval
Bing
Web query classification
Information science
Information retrieval
Learning to rank

Add to Reading List

Source URL: radlinski.org

Download Document from Source Website

File Size: 177,49 KB

Share Document on Facebook

Similar Documents

Learning to select a ranking function Jie Peng, Craig Macdonald, and Iadh Ounis Department of Computing Science, University of Glasgow, G12 8QQ, UK {pj, craigm, ounis}@dcs.gla.ac.uk

DocID: 1slqY - View Document

Machine learning / Statistics / Learning / Regression analysis / Structured prediction / Support vector machines / Statistical classification / Ordinal regression / Loss function / Mathematical optimization / Convex optimization / Loss functions for classification

Large-margin Structured Learning for Link Ranking Stephen H. Bach Bert Huang Lise Getoor Department of Computer Science University of Maryland College Park, MD 20742

DocID: 1qSCZ - View Document

Probability distributions / Estimation theory / Bayesian statistics / Likelihood function / Maximum likelihood estimation / Normal distribution / Weibull distribution / Hermite distribution / Laplace distribution

IMPROVING THE RANKING SYSTEM FOR WOMEN’S PROFESIONAL TENNIS By MAYA MILADINOVIC

DocID: 1pIb9 - View Document

Regression analysis / Structured prediction / Support vector machines / Statistical classification / Machine learning / Ordinal regression / Loss function / Mathematical optimization / Hinge loss / Loss functions for classification

Large-margin Structured Learning for Link Ranking Stephen H. Bach Bert Huang Lise Getoor Department of Computer Science University of Maryland College Park, MD 20742

DocID: 1puOe - View Document

Algebra / Mathematics / Information science / Linear algebra / Matrix theory / Recommender systems / Statistical theory / Collaborative filtering / Machine learning / Loss function / Singular value decomposition / Artificial neural network

BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme {srendle, freudenthaler, gantner, schmidt-thieme}@ismll.de Machine Learning Lab, Un

DocID: 1ninV - View Document