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» Some new directions in graph-based semi-supervised learning
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AAAI
2006
15 years 1 months ago
Active Learning with Near Misses
Assume that we are trying to build a visual recognizer for a particular class of objects--chairs, for example--using existing induction methods. Assume the assistance of a human t...
Nela Gurevich, Shaul Markovitch, Ehud Rivlin
85
Voted
AAAI
1996
15 years 1 months ago
Post-Analysis of Learned Rules
Rule induction research implicitly assumes that after producing the rules from a dataset, these rules will be used directly by an expert system or a human user. In real-life appli...
Bing Liu, Wynne Hsu
119
Voted
JMLR
2010
143views more  JMLR 2010»
14 years 10 months ago
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
We extend the well-known BFGS quasi-Newton method and its memory-limited variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by ge...
Jin Yu, S. V. N. Vishwanathan, Simon Günter, ...
107
Voted
UAI
2000
15 years 1 months ago
Gaussian Process Networks
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
Nir Friedman, Iftach Nachman
VLDB
1998
ACM
147views Database» more  VLDB 1998»
15 years 4 months ago
Scalable Techniques for Mining Causal Structures
Mining for association rules in market basket data has proved a fruitful areaof research. Measures such as conditional probability (confidence) and correlation have been used to i...
Craig Silverstein, Sergey Brin, Rajeev Motwani, Je...