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113
Voted
ALT
2001
Springer
16 years 13 days ago
Learning by Switching Type of Information
The present work is dedicated to the study of modes of data-presentation in the range between text and informant within the framework of inductive inference. In this study, the le...
Sanjay Jain, Frank Stephan
ICML
2005
IEEE
16 years 4 months ago
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
Mark Girolami, Simon Rogers
115
Voted
ML
2006
ACM
15 years 3 months ago
A Unified View on Clustering Binary Data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
Tao Li
123
Voted
CIS
2004
Springer
15 years 9 months ago
Knowledge Maintenance on Data Streams with Concept Drifting
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
Juggapong Natwichai, Xue Li
ICML
1996
IEEE
16 years 4 months ago
Discretizing Continuous Attributes While Learning Bayesian Networks
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
Moisés Goldszmidt, Nir Friedman