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Drinfeld modular polynomials in higher rank
 J. Number Theory
"... We study modular polynomials classifying cyclic isogenies between Drinfeld modules of arbitrary rank over the ring Fq[T]. 1 ..."
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Cited by 2 (2 self)
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We study modular polynomials classifying cyclic isogenies between Drinfeld modules of arbitrary rank over the ring Fq[T]. 1
Support vector machines: Training and applications
 A.I. MEMO 1602, MIT A. I. LAB
, 1997
"... The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and MultiLayer Perc ..."
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Cited by 223 (3 self)
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The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi
Generalization in the XCS Classifier System
, 1998
"... This paper studies two changes to XCS, a classifier system in which fitness is based on prediction accuracy and the genetic algorithm takes place in environmental niches. The changes were aimed at increasing XCS's tendency to evolve accurate, maximally general classifiers and were tested o ..."
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Cited by 86 (11 self)
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This paper studies two changes to XCS, a classifier system in which fitness is based on prediction accuracy and the genetic algorithm takes place in environmental niches. The changes were aimed at increasing XCS's tendency to evolve accurate, maximally general classifiers and were tested
Simplified Support Vector Decision Rules
, 1996
"... A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors, and by a set of corresponding weights. An SVM is also characterized by a kernel function. Choice of the kernel determines whether the resulting SVM is a polynomial cla ..."
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Cited by 184 (5 self)
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A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors, and by a set of corresponding weights. An SVM is also characterized by a kernel function. Choice of the kernel determines whether the resulting SVM is a polynomial
Classifying Ehrhart polynomials of lattice simplices
, 1994
"... es of this element in the group (i.e. the elements of the subgroup generated by z), one on each line, and write the sum of each line, divided by 17, on the right (call this number the weight). Now, count the number of 0's appearing in the right hand column (always just one), the number of 1&apo ..."
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's and so on. This is the h vector of a certain simplex. That is, the ith coordinate of the deltavector is the number of lines with weight i. The goal is to classify all vectors that arise in this way. Solving this would amount to classifying the deltavectors of ddimensional lattice
On the Influence of the Kernel on the Consistency of Support Vector Machines
 Journal of Machine Learning Research
, 2001
"... In this article we study the generalization abilities of several classifiers of support vector machine (SVM) type using a certain class of kernels that we call universal. It is shown that the soft margin algorithms with universal kernels are consistent for a large class of classification problems ..."
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Cited by 212 (21 self)
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In this article we study the generalization abilities of several classifiers of support vector machine (SVM) type using a certain class of kernels that we call universal. It is shown that the soft margin algorithms with universal kernels are consistent for a large class of classification
Generalized principal component analysis (GPCA)
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2003
"... This paper presents an algebrogeometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a ..."
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Cited by 206 (36 self)
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at a data point give normal vectors to the subspace passing through the point. When the number of subspaces is known, we show that these polynomials can be estimated linearly from data; hence, subspace segmentation is reduced to classifying one point per subspace. We select these points optimally from
Low Complexity Speaker Verification Using A Polynomialbased Classifier
, 1999
"... With the prevalence of the information age, privacy issues are forefront in today's society. As such, biometrics, such as speaker verification, are viewed as new tools for solving these security concerns. Often, personal authentication must take placefrom portable devices, such as cellular tele ..."
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issue. Low complexity authentication techniques address both functions. Presented in this paper is such a speaker verification system. It uses a low complexity, high accuracy technology based upon averaging a discriminative polynomial classifier over time. An efficient method ...
Results 21  30
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1,049