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ICTAI
2006
IEEE
15 years 9 months ago
MI-Winnow: A New Multiple-Instance Learning Algorithm
We present MI-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a co...
Sharath R. Cholleti, Sally A. Goldman, Rouhollah R...
NIPS
2004
15 years 4 months ago
Learning Gaussian Process Kernels via Hierarchical Bayes
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
Anton Schwaighofer, Volker Tresp, Kai Yu
ICML
2003
IEEE
16 years 4 months ago
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
16 years 3 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
ACMSE
2010
ACM
14 years 10 months ago
Learning to rank using 1-norm regularization and convex hull reduction
The ranking problem appears in many areas of study such as customer rating, social science, economics, and information retrieval. Ranking can be formulated as a classification pro...
Xiaofei Nan, Yixin Chen, Xin Dang, Dawn Wilkins