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Constructing XofN Attributes for Decision Tree Learning
 Machine Learning
, 1998
"... . While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, XofN. An XofN representation is a set containing one or more attributevalue pairs. ..."
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Cited by 20 (0 self)
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. While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, XofN. An XofN representation is a set containing one or more attributevalue pairs
Constructing Nominal XofN Attributes
 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence
, 1995
"... Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations. An XofN is a set containing one or more attributevalue pairs. For a g ..."
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Cited by 15 (6 self)
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Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations. An XofN is a set containing one or more attributevalue pairs. For a
Constructing Nominal XofN Attributes
"... Most constructive induction researchers focus only on new boolean attributes This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations An XofN is a Bet containing one or more attributevalue pairs For a give ..."
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Most constructive induction researchers focus only on new boolean attributes This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of XofN representations An XofN is a Bet containing one or more attributevalue pairs For a
Continuousvalued XofN Attributes Versus Nominal XofN Attributes for Constructive Induction: A Case Study
 for Young Computer Scientists, Peking University
, 1995
"... : An XofN is a set containing one or more attributevalue pairs. For a given instance, its value corresponds to the number of its attributevalue pairs that are true. In this paper, we explore the characteristics and performance of continuousvalued XofN attributes versus nominal XofN attribute ..."
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Cited by 5 (4 self)
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continuousvalued XofNs are described. Continuousvalued XofNs perform better than nominal ones on domains that need XofNs with only one cut point. On domains that need XofN representations with more than one cut point, nominal XofNs perform better than continuousvalued ones. Experimental results
Constructing XofN attributes with a genetic algorithm
 In Proc. of the Genetic and Evolutionary Computation Conference
, 2002
"... The predictive accuracy obtained by a classification algorithm is strongly dependent on the quality of the attributes of the data being mined. When the attributes are little relevant for predicting the class of a record, the predictive accuracy will tend to be low. To combat this problem, a natural ..."
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Cited by 3 (0 self)
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. A more expressive representation is XofN [Zheng 1995]. An XofN condition consists of a set of N attributevalue pairs. The value of an XofN condition for a given example
For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1norm Solution is also the Sparsest Solution
 Comm. Pure Appl. Math
, 2004
"... We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so that ..."
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Cited by 568 (10 self)
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that for large n, and for all Φ’s except a negligible fraction, the following property holds: For every y having a representation y = Φα0 by a coefficient vector α0 ∈ R m with fewer than ρ · n nonzeros, the solution α1 of the ℓ 1 minimization problem min �x�1 subject to Φα = y is unique and equal to α0
Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition
 in Conference Record of The TwentySeventh Asilomar Conference on Signals, Systems and Computers
, 1993
"... In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. aiEne (wa.velet) frames. We propoeea modification to the Matching Pursuit algorithm of Mallat and Zhang (199 ..."
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Cited by 637 (1 self)
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recursively. where fk is the current approximation, and Rkf the current residual (error). Using initial values ofR0f = 1, fo = 0, and k = 1, the MP algorithm is comprised of the following steps,.,.41) Compute the innerproducts {(Rkf,z)}. (H) Find flki such that (III) Set, I(R*f,1:n 1+,)l asupl
Laplacian eigenmaps and spectral techniques for embedding and clustering.
 Proceeding of Neural Information Processing Systems,
, 2001
"... Abstract Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in ..."
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Cited by 668 (7 self)
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retrieval and data mining, one is often confronted with intrinsically low dimensional data lying in a very high dimensional space. For example, gray scale n x n images of a fixed object taken with a moving camera yield data points in rn: n2 . However , the intrinsic dimensionality of the space of all images
A Comparison of Constructive Induction with Different Types of New Attribute
, 1996
"... : This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, search starting points, evaluation fun ..."
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Cited by 2 (1 self)
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: This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, search starting points, evaluation
Effects of Different Types of New Attribute on Constructive Induction
, 1996
"... This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping crite ..."
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This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, MofN, and XofN representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping
Results 1  10
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