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» Hedging predictions in machine learning
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BMCBI
2006
173views more  BMCBI 2006»
15 years 4 months ago
Kernel-based distance metric learning for microarray data classification
Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
Huilin Xiong, Xue-wen Chen
ALT
1995
Springer
15 years 8 months ago
Learning Unions of Tree Patterns Using Queries
This paper characterizes the polynomial time learnability of TPk, the class of collections of at most k rst-order terms. A collection in TPk de nes the union of the languages de n...
Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara
ICML
1999
IEEE
16 years 5 months ago
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
ALS
2003
Springer
15 years 9 months ago
Not Everything We Know We Learned
This is foremost a methodological contribution. It focuses on the foundation of anticipation and the pertinent implications that anticipation has on learning (theory and experiment...
Mihai Nadin
CIKM
2008
Springer
15 years 6 months ago
Learning a two-stage SVM/CRF sequence classifier
Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "ca...
Guilherme Hoefel, Charles Elkan