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» A Framework for Multiple-Instance Learning
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COLT
1999
Springer
15 years 11 months ago
Covering Numbers for Support Vector Machines
—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Until recently, the only bounds on th...
Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Ro...
GIS
1992
ACM
15 years 11 months ago
Machine Induction of Geospatial Knowledge
Machine learning techniques such as tree induction have become accepted tools for developing generalisations of large data sets, typically for use with production rule systems in p...
Peter A. Whigham, Robert I. McKay, J. R. Davis
206
Voted
AVSS
2006
IEEE
15 years 10 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
199
Voted
ECML
2006
Springer
15 years 10 months ago
Prioritizing Point-Based POMDP Solvers
Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods such as PBVI, Perseus, and HSVI, which quickly converge to an approximate so...
Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony
212
Voted
COLT
2000
Springer
15 years 10 months ago
The Role of Critical Sets in Vapnik-Chervonenkis Theory
In the present paper, we present the theoretical basis, as well as an empirical validation, of a protocol designed to obtain effective VC dimension estimations in the case of a si...
Nicolas Vayatis