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» Evaluating algorithms that learn from data streams
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TIT
2008
76views more  TIT 2008»
15 years 6 months 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
ICDM
2003
IEEE
134views Data Mining» more  ICDM 2003»
15 years 11 months ago
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conve...
Bianca Zadrozny, John Langford, Naoki Abe
DMIN
2006
133views Data Mining» more  DMIN 2006»
15 years 8 months ago
A Fuzzy Neural Based Data Classification System
Data mining has emerged to be a very important research area that helps organizations make good use of the tremendous amount of data they have. In data classification tasks, fuzzy ...
Luong Trung Tuan, Suet Peng Yong
ISCAS
2003
IEEE
117views Hardware» more  ISCAS 2003»
15 years 11 months ago
Learning temporal correlations in biologically-inspired aVLSI
Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firin...
Adria Bofill-i-Petit, Alan F. Murray
PAMI
2008
135views more  PAMI 2008»
15 years 6 months ago
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
In this paper, we develop a new effective multiple kernel learning algorithm. First, we map the input data into m different feature spaces by m empirical kernels, where each genera...
Zhe Wang, Songcan Chen, Tingkai Sun