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» Learning Generative Models with the Up-Propagation Algorithm
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HIS
2007
13 years 11 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin
NECO
2010
154views more  NECO 2010»
13 years 8 months ago
Role of Homeostasis in Learning Sparse Representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that ...
Laurent U. Perrinet
ICCV
2009
IEEE
15 years 3 months ago
Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories
Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a partbased probabilistic representation [31], we propose a new 3D object...
Hao Su, Min Sun, Li Fei-Fei, Silvio Savarese
ICML
2009
IEEE
14 years 11 months ago
Structure learning with independent non-identically distributed data
There are well known algorithms for learning the structure of directed and undirected graphical models from data, but nearly all assume that the data consists of a single i.i.d. s...
Robert E. Tillman
ESANN
2006
13 years 11 months ago
Margin based Active Learning for LVQ Networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...