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» Evaluating learning algorithms and classifiers
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127
Voted
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
13 years 3 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
MIR
2005
ACM
198views Multimedia» more  MIR 2005»
15 years 6 months ago
Semi-automatic video annotation based on active learning with multiple complementary predictors
In this paper, we will propose a novel semi-automatic annotation scheme for video semantic classification. It is well known that the large gap between high-level semantics and low...
Yan Song, Xian-Sheng Hua, Li-Rong Dai, Meng Wang
115
Voted
KDD
2010
ACM
257views Data Mining» more  KDD 2010»
15 years 4 months ago
Multi-task learning for boosting with application to web search ranking
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing...
Olivier Chapelle, Pannagadatta K. Shivaswamy, Srin...
97
Voted
ROBOCUP
2007
Springer
102views Robotics» more  ROBOCUP 2007»
15 years 6 months ago
Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents
This paper describes the design and implementation of robotic agents for the RoboCup Simulation 2D category that learns using a recently proposed Heuristic Reinforcement Learning a...
Luiz A. Celiberto, Carlos H. C. Ribeiro, Anna Hele...
ECAI
2008
Springer
15 years 2 months ago
Online Rule Learning via Weighted Model Counting
Online multiplicative weight-update learning algorithms, such as Winnow, have proven to behave remarkably for learning simple disjunctions with few relevant attributes. The aim of ...
Frédéric Koriche