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» Perceptual Learning and Abstraction in Machine Learning
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TCS
2008
14 years 9 months ago
Kernel methods for learning languages
This paper studies a novel paradigm for learning formal languages from positive and negative examples which consists of mapping strings to an appropriate highdimensional feature s...
Leonid Kontorovich, Corinna Cortes, Mehryar Mohri
ICRA
2008
IEEE
229views Robotics» more  ICRA 2008»
15 years 4 months ago
Learning of moving cast shadows for dynamic environments
Abstract— We propose a novel online framework for detecting moving shadows in video sequences using statistical learning techniques. In this framework, Support Vector Machines ar...
Ajay J. Joshi, Nikolaos Papanikolopoulos
ECML
2005
Springer
15 years 3 months ago
Learning from Positive and Unlabeled Examples with Different Data Distributions
Abstract. We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive exam...
Xiaoli Li, Bing Liu
INCDM
2010
Springer
146views Data Mining» more  INCDM 2010»
15 years 1 months ago
Learning from Humanoid Cartoon Designs
Abstract. Character design is a key ingredient to the success of any comicbook, graphic novel, or animated feature. Artists typically use shape, size and proportion as the first de...
Md. Tanvirul Islam, Kaiser Md. Nahiduzzaman, Why Y...
CONCUR
2006
Springer
15 years 1 months ago
Minimization, Learning, and Conformance Testing of Boolean Programs
Boolean programs with recursion are convenient abstractions of sequential imperative programs, and can be represented as recursive state machines (RSMs) or pushdown automata. Motiv...
Viraj Kumar, P. Madhusudan, Mahesh Viswanathan