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» On learning with dissimilarity functions
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ICML
2005
IEEE
16 years 7 months ago
A brain computer interface with online feedback based on magnetoencephalography
The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG)...
Bernhard Schölkopf, Hubert Preißl, J&uu...
ICML
1998
IEEE
16 years 7 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
163
Voted
KDD
2009
ACM
207views Data Mining» more  KDD 2009»
16 years 7 months ago
DynaMMo: mining and summarization of coevolving sequences with missing values
Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn ...
Lei Li, James McCann, Nancy S. Pollard, Christos F...
STOC
2003
ACM
154views Algorithms» more  STOC 2003»
16 years 7 months ago
Boosting in the presence of noise
Boosting algorithms are procedures that "boost" low-accuracy weak learning algorithms to achieve arbitrarily high accuracy. Over the past decade boosting has been widely...
Adam Kalai, Rocco A. Servedio
ALT
2004
Springer
16 years 3 months ago
New Revision Algorithms
A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in ...
Judy Goldsmith, Robert H. Sloan, Balázs Sz&...