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» On the Generalization Ability of On-Line Learning Algorithms
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FLAIRS
2004
11 years 6 months ago
A Faster Algorithm for Generalized Multiple-Instance Learning
In our prior work, we introduced a generalization of the multiple-instance learning (MIL) model in which a bag's label is not based on a single instance's proximity to a...
Qingping Tao, Stephen D. Scott
PAMI
2012
9 years 7 months ago
Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem
Abstract—We consider two desired properties of learning algorithms: sparsity and algorithmic stability. Both properties are believed to lead to good generalization ability. We sh...
Huan Xu, Constantine Caramanis, Shie Mannor
ICASSP
2010
IEEE
11 years 5 months ago
A general formalism for the analysis of distributed algorithms
The major contribution of this paper is the presentation of a general unifying description of distributed algorithms allowing to map local, node-based, algorithms onto a single gl...
Ondrej Sluciak, Thibault Hilaire, Markus Rupp
IJCNN
2006
IEEE
11 years 11 months ago
Generalization Improvement in Multi-Objective Learning
— Several heuristic methods have been suggested for improving the generalization capability in neural network learning, most of which are concerned with a single-objective (SO) l...
Lars Gräning, Yaochu Jin, Bernhard Sendhoff
ICML
2009
IEEE
12 years 6 months ago
A least squares formulation for a class of generalized eigenvalue problems in machine learning
Many machine learning algorithms can be formulated as a generalized eigenvalue problem. One major limitation of such formulation is that the generalized eigenvalue problem is comp...
Liang Sun, Shuiwang Ji, Jieping Ye
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