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» Selective Attention Improves Learning
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77
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TIT
2008
76views more  TIT 2008»
15 years 18 days ago
Improved Risk Tail Bounds for On-Line Algorithms
We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incrementally on the training data. Our result i...
Nicolò Cesa-Bianchi, Claudio Gentile
102
Voted
BLISS
2008
IEEE
15 years 7 months ago
Effecting an Improvement to the Fitness Function. How to Evolve a More Identifiable Face
Constructing the face of a criminal from the selection of individual facial parts is a hard task. We have been working on a new system called EvoFIT that involves the selection an...
Charlie D. Frowd, Joanne Park, Alex H. McIntyre, V...
99
Voted
ICDM
2007
IEEE
138views Data Mining» more  ICDM 2007»
15 years 7 months ago
Bandit-Based Algorithms for Budgeted Learning
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
Kun Deng, Chris Bourke, Stephen D. Scott, Julie Su...
109
Voted
ECIR
2008
Springer
15 years 2 months ago
Robust Query-Specific Pseudo Feedback Document Selection for Query Expansion
In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-ranked documents are selected as feedback to build a new expansion query model. ...
Qiang Huang, Dawei Song, Stefan M. Rüger
100
Voted
ILP
2005
Springer
15 years 6 months ago
Predicate Selection for Structural Decision Trees
Abstract. We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in cl...
Kee Siong Ng, John W. Lloyd