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ALT
2010
Springer
13 years 6 months ago
Online Multiple Kernel Learning: Algorithms and Mistake Bounds
Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing ...
Rong Jin, Steven C. H. Hoi, Tianbao Yang
SIAMCOMP
2008
140views more  SIAMCOMP 2008»
13 years 4 months ago
The Forgetron: A Kernel-Based Perceptron on a Budget
Abstract. The Perceptron algorithm, despite its simplicity, often performs well in online classification tasks. The Perceptron becomes especially effective when it is used in conju...
Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer
NIPS
2001
13 years 6 months ago
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
Roni Khardon, Dan Roth, Rocco A. Servedio
NIPS
2004
13 years 6 months ago
Mistake Bounds for Maximum Entropy Discrimination
We establish a mistake bound for an ensemble method for classification based on maximizing the entropy of voting weights subject to margin constraints. The bound is the same as a ...
Philip M. Long, Xinyu Wu
ICML
2004
IEEE
14 years 5 months ago
Online and batch learning of pseudo-metrics
We describe and analyze an online algorithm for supervised learning of pseudo-metrics. The algorithm receives pairs of instances and predicts their similarity according to a pseud...
Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng