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ICML
2009
IEEE
16 years 2 months ago
Structure learning with independent non-identically distributed data
There are well known algorithms for learning the structure of directed and undirected graphical models from data, but nearly all assume that the data consists of a single i.i.d. s...
Robert E. Tillman
FOCS
1990
IEEE
15 years 5 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum
ACL
2008
15 years 2 months ago
A Supervised Learning Approach to Automatic Synonym Identification Based on Distributional Features
Distributional similarity has been widely used to capture the semantic relatedness of words in many NLP tasks. However, various parameters such as similarity measures must be hand...
Masato Hagiwara
COLT
2010
Springer
14 years 11 months ago
Learning to Create is as Hard as Learning to Appreciate
We explore the relationship between a natural notion of unsupervised learning studied by Kearns et al. (STOC '94), which we call here "learning to create" (LTC), an...
David Xiao
ICDM
2006
IEEE
130views Data Mining» more  ICDM 2006»
15 years 7 months ago
Boosting for Learning Multiple Classes with Imbalanced Class Distribution
Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which ...
Yanmin Sun, Mohamed S. Kamel, Yang Wang 0007