scholarly journals Combining classifiers by constructive induction

1998 ◽  
pp. 178-189 ◽  
Author(s):  
João Gama
Keyword(s):  
Constructive Induction ◽  
Combining Classifiers

A genetic encoding approach for learning methods for combining classifiers

2009 ◽  
Vol 36 (4) ◽  
pp. 7510-7514 ◽  
Author(s):  
Loris Nanni ◽  
Alessandra Lumini
Keyword(s):  
Learning Methods ◽  
Combining Classifiers ◽  
Genetic Encoding

scholarly journals Combining Classifiers to Extract Web Data

2015 ◽  
Author(s):  
Qiang Chu ◽  
Yongquan Dong ◽  
Ping Ling
Keyword(s):  
Web Data ◽  
Combining Classifiers

The development of features in object concepts

1998 ◽  
Vol 21 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Philippe G. Schyns ◽  
Robert L. Goldstone ◽  
Jean-Pierre Thibaut
Keyword(s):  
Cognitive Processes ◽  
Category Learning ◽  
Cognitive Process ◽  
Conceptual Development ◽  
Object Categorization ◽  
Constructive Induction ◽  
Fixed Space ◽  
Functional Features ◽  
High Level ◽  
Perceptual Changes

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction.

Is independence good for combining classifiers?

2002 ◽  
Author(s):  
L.I. Kuncheva ◽  
C.J. Whitaker ◽  
C.A. Shipp ◽  
R.P.W. Duin
Keyword(s):  
Combining Classifiers ◽  
Good For

*Constructive Induction

2007 ◽  
pp. 213-226
Author(s):  
Igor Kononenko ◽  
Matjaž Kukar
Keyword(s):  
Constructive Induction
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