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SupportVector Networks
 Machine Learning
, 1995
"... The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special pr ..."
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Cited by 3703 (35 self)
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The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
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Cited by 727 (1 self)
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We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision
Gradientbased learning applied to document recognition
 Proceedings of the IEEE
, 1998
"... Multilayer neural networks trained with the backpropagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradientbased learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
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Cited by 1533 (84 self)
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Multilayer neural networks trained with the backpropagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradientbased learning algorithms can be used to synthesize a complex decision surface that can classify
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
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Cited by 633 (15 self)
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to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a
Spherical Decision Surfaces Using Conformal
"... Abstract. In this paper a special higher order neuron, the hypersphere neuron, is introduced. By embedding Euclidean space in a conformal space, hyperspheres can be expressed as vectors. The scalar product of points and spheres in conformal space, gives a measure for how far a point lies inside or o ..."
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or outside a hypersphere. It will be shown that a hypersphere neuron may be implemented as a perceptron with two bias inputs. By using hyperspheres instead of hyperplanes as decision surfaces, a reduction in computational complexity can be achieved for certain types of problems. Furthermore, it will be shown
Efficient exact stochastic simulation of chemical systems with many species and many channels
 J. Phys. Chem. A
, 2000
"... There are two fundamental ways to view coupled systems of chemical equations: as continuous, represented by differential equations whose variables are concentrations, or as discrete, represented by stochastic processes whose variables are numbers of molecules. Although the former is by far more comm ..."
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Cited by 427 (5 self)
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common, systems with very small numbers of molecules are important in some applications (e.g., in small biological cells or in surface processes). In both views, most complicated systems with multiple reaction channels and multiple chemical species cannot be solved analytically. There are exact numerical
On Heuristic Mapping of Decision Surfaces for PostEvaluation Analysis
, 1997
"... The value for decision making of highquality postsolution analysis of decisionsupporting models can hardly be overestimated. Candlelighting analysis �CLA � takes this notion very seriously and brings to the table a series of computational approaches for gaining insight into decision problems by p ..."
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Cited by 11 (9 self)
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by performing heuristic mapping of decision surfaces for management science models. The principal aims of this paper are to present, illustrate, and demonstrate the value, and extensive range, of candlelighting analysis �CLA � ideas in the context of a new decision support system, CLAPNT. In addition
Optimal decision surfaces in LVQ1 classification of patterns
, 1993
"... Kohonen's LVQ1 procedure is widely used for the classification of patterns in a multiclass distribution. This algorithm approximates the probability densities of vectors in each class, by updating reference vectors at each presentation of an input pattern. It will however be shown in this ..."
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in this paper that the Bayes classification is only approximated by the decision surfaces of LVQ1 in special cases of probability densities; we will show how to set the decision surfaces in a more general case, and how the relative number of reference vectors in each class can comply with their a priori
Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning
"... Abstract Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of geneticallyinduced classification trees, which employ linear discriminants as the partitioning function at each internal node ..."
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optimisation concerns maximising the decisionsurface margin that is defined to be the smallest distance between the decisionsurface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository suggest that model generalisation benefits
A NEW CLASS OF DECISION SURFACES BASED ON THE MINIMIZATION OF WITHIN CLASS VARIANCE
"... In this paper a modified class of Support Vector Machine (SVM) inspired from the optimization of Fisher's discriminant ratio is presented. The modified class ofSVM is used in order to find decision surfaces by solving the corresponding optimization problem in arbitrary Hilbert spaces, defined ..."
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In this paper a modified class of Support Vector Machine (SVM) inspired from the optimization of Fisher's discriminant ratio is presented. The modified class ofSVM is used in order to find decision surfaces by solving the corresponding optimization problem in arbitrary Hilbert spaces, defined
Results 1  10
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2,656