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» A Study of Empirical Learning for an Involved Problem
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101
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JMLR
2010
123views more  JMLR 2010»
14 years 6 months ago
Inductive Principles for Restricted Boltzmann Machine Learning
Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with in...
Benjamin Marlin, Kevin Swersky, Bo Chen, Nando de ...
SAFECOMP
2000
Springer
15 years 3 months ago
Expert Error: The Case of Trouble-Shooting in Electronics
An expert trouble-shooter is a subject who has a great deal of experience in his activity that allows him or her to be very efficient. However, the large amount of problems he or s...
Denis Besnard
ACML
2009
Springer
15 years 3 months ago
Max-margin Multiple-Instance Learning via Semidefinite Programming
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
Yuhong Guo
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
15 years 4 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
84
Voted
ICML
2002
IEEE
16 years 17 days ago
Multi-Instance Kernels
Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, rese...
Adam Kowalczyk, Alex J. Smola, Peter A. Flach, Tho...