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KDD
2010
ACM
249views Data Mining» more  KDD 2010»
13 years 9 months ago
Semi-supervised sparse metric learning using alternating linearization optimization
In plenty of scenarios, data can be represented as vectors mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining app...
Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu, ...
AAAI
1997
13 years 9 months ago
Worst-Case Absolute Loss Bounds for Linear Learning Algorithms
The absolute loss is the absolute difference between the desired and predicted outcome. I demonstrateworst-case upper bounds on the absolute loss for the perceptron algorithm and ...
Tom Bylander
NIPS
2000
13 years 9 months ago
Learning Switching Linear Models of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...
Vladimir Pavlovic, James M. Rehg, John MacCormick
AUTOMATICA
2007
103views more  AUTOMATICA 2007»
13 years 7 months ago
Adaptive learning control of linear systems by output error feedback
: This paper addresses the problem of designing an output error feedback tracking control for single-input, single-output, minimum phase, observable linear systems. The reference o...
Stefano Liuzzo, Riccardo Marino, Patrizio Tomei
PKDD
2009
Springer
148views Data Mining» more  PKDD 2009»
14 years 2 months ago
Feature Selection by Transfer Learning with Linear Regularized Models
Abstract. This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervisio...
Thibault Helleputte, Pierre Dupont