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ML
2000
ACM
185views Machine Learning» more  ML 2000»
13 years 8 months ago
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms
Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the ca...
Tjen-Sien Lim, Wei-Yin Loh, Yu-Shan Shih
ICML
1996
IEEE
14 years 9 months ago
Learning Evaluation Functions for Large Acyclic Domains
Some of the most successful recent applications of reinforcement learning have used neural networks and the TD algorithm to learn evaluation functions. In this paper, we examine t...
Justin A. Boyan, Andrew W. Moore
BMCBI
2010
165views more  BMCBI 2010»
13 years 8 months ago
MTar: a computational microRNA target prediction architecture for human transcriptome
Background: MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in importa...
Vinod Chandra, Reshmi Girijadevi, Achuthsankar S. ...
ICIP
2003
IEEE
14 years 10 months ago
Statistical learning for effective visual information retrieval
For effective retrieval of visual information, statistical learning plays a pivotal role. Statistical learning in such a context faces at least two major mathematical challenges: ...
Edward Y. Chang, Beitao Li, Gang Wu, Kingshy Goh
ICPR
2000
IEEE
14 years 9 months ago
General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of D
An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error in [3]. The bias/vari...
Jakob Vogdrup Hansen, Tom Heskes