Victor Lavrenko

IR20.11 Summary

IR20.8 Learning to rank with an SVM

IR20.10 Learning to rank with click data

IR20.9 Learning to rank: features

IR20.7 Learning to rank for Information Retrieval

IR20.3 Passive-aggressive algorithm (PA)

IR20.5 SVM наглядно объяснено

IR20.2 Классификация с большой маржой

Центроидный классификатор IR20.1

IR20.4 Convergence of the PA algorithm

IR20.6 Последовательная минимальная оптимизация (SMO)

LM.9 Jelinek-Mercer smoothing

LM.7 Good-Turing estimate

LM.4 The unigram model (urn model)

LM.14 Issues to consider

LM.8 Interpolation with background model

LM.2 What is a language model?

LM.10 Dirichlet smoothing

LM.11 Leave-one-out smoothing

LM.5 Zero-frequency problem

LM.3 Query likelihood ranking

LM.1 Overview

LM.6 Laplace correction and absolute discounting

LM.12 Smoothing and inverse document frequency

BIR.10 Estimation with relevant examples

BIR.17 Modelling term frequency

BIR.16 Linked dependence assumption

BIR.12 Example

BIR.3 Probability of relevance