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528
A tutorial on support vector machines for pattern recognition
 Data Mining and Knowledge Discovery
, 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and nonseparable data, working through a nontrivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
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Cited by 3393 (12 self)
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The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and nonseparable data, working through a nontrivial example in detail. We describe a mechanical analogy, and discuss when
(Tutorial)
"... Neural networks and fuzzy inference systems have been widely used in several intelligent multimedia applications. Artificial Neural Network (ANN) learns from scratch by adjusting the interconnections between layers. Fuzzy Inference System (FIS) is a popular computing framework based on the concept o ..."
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of fuzzy set theory, fuzzy ifthen rules, and fuzzy reasoning. Integrating ANN and FIS have attracted the growing interest of researchers due to the growing need of adaptive intelligent systems to meet the real world requirements. This tutorial paper starts with some basic theoretical aspects of ANN
Tabu Search: A Tutorial
 Interfaces
, 1990
"... Tabu search is a "higher level " heuristic procedure for solving optimization problems, designed to guide other methods (or their component processes) to escape the trap of local optimality. Tabu search has obtained optimal and near optimal solutions to a wide variety of classical and prac ..."
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Cited by 145 (2 self)
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be integrated with branchandbound and cutting plane procedures, and it has the ability to start with a simple implementation that can be upgraded over time to incorporate more advanced or specialized elements. T abu search is a metaheuristic that can to prevent them from becoming trapped at be superimposed
A Tutorial on Geometric Programming
"... A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can solve even largescale GPs extremely efficiently and reliably; at the same time a number of practical problems ..."
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Cited by 123 (11 self)
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or design problem, in GP format. In the best case, this formulation is exact; when this isn’t possible, we settle for an approximate formulation. This tutorial paper collects together in one place the basic background material needed to do GP modeling. We start with the basic definitions and facts, and some
polymake: a Framework for Analyzing Convex Polytopes
, 1999
"... polymake is a software tool designed for the algorithmic treatment of polytopes and polyhedra. We give an overview of the functionality as well as of the structure. This paper can be seen as a first approximation to a polymake handbook. The tutorial starts with the very basics and ends up with a few ..."
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Cited by 168 (21 self)
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polymake is a software tool designed for the algorithmic treatment of polytopes and polyhedra. We give an overview of the functionality as well as of the structure. This paper can be seen as a first approximation to a polymake handbook. The tutorial starts with the very basics and ends up with a
Tutorial: Getting Started with MART in R
 in R,” tutorial
, 2002
"... Multiple additive regression trees (MART) is a methodology for predictive data mining (regression and classification). This note illustrates the use of the R/MART interface. It is intended to be a tutorial introduction. Minimal knowledge concerning the technical details of the MART methodology or ..."
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Cited by 3 (0 self)
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Multiple additive regression trees (MART) is a methodology for predictive data mining (regression and classification). This note illustrates the use of the R/MART interface. It is intended to be a tutorial introduction. Minimal knowledge concerning the technical details of the MART methodology
Tutorial: Getting Started with MART in Splus
, 1999
"... Multiple additive regression trees (MART) is a methodology for predictive data mining (regression and classification). This note illustrates the use of the MART/Splus interface. It is intended to be a tutorial introduction. Minimal knowledge concerning the technical details of the methodology or the ..."
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Cited by 1 (0 self)
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Multiple additive regression trees (MART) is a methodology for predictive data mining (regression and classification). This note illustrates the use of the MART/Splus interface. It is intended to be a tutorial introduction. Minimal knowledge concerning the technical details of the methodology
An R Tutorial 1. Starting Out
"... R is an interactive environment for statistical computing and graphics. This tutorial will assume usage of R 2.0.0 on a PC. However, except in rare situations, these commands will work in R on UNIX and Macintosh machines as well as in SPlus on any platform. R can be freely downloaded at ..."
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R is an interactive environment for statistical computing and graphics. This tutorial will assume usage of R 2.0.0 on a PC. However, except in rare situations, these commands will work in R on UNIX and Macintosh machines as well as in SPlus on any platform. R can be freely downloaded at
Tutorial: Getting Started with MART in Splus
, 1999
"... Multiple additive regression trees (MART) is a methodology for predictive data mining (regression and classification). This note illustrates the use of the MART/Splus interface. It is intended to be a tutorial introduction. Minimal knowledge concerning the technical details of the methodology or the ..."
Abstract
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Multiple additive regression trees (MART) is a methodology for predictive data mining (regression and classification). This note illustrates the use of the MART/Splus interface. It is intended to be a tutorial introduction. Minimal knowledge concerning the technical details of the methodology
Prediction With Gaussian Processes: From Linear Regression To Linear Prediction And Beyond
 Learning and Inference in Graphical Models
, 1997
"... The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. Th ..."
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Cited by 228 (4 self)
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The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters
Results 1  10
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528