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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 3621 (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
Generating input vectors for Neural Nets
"... eparate handwritten characters into numerals and letters. 1 Neural Nets: 3 2 A A A A A A B B B B B B A/B classification Suppose now there are 4 classes A, B, C, D and that they are separable by two planes in pattern space D D D A A A A B C C C C C C B B B pattern space ..."
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eparate handwritten characters into numerals and letters. 1 Neural Nets: 3 2 A A A A A A B B B B B B A/B classification Suppose now there are 4 classes A, B, C, D and that they are separable by two planes in pattern space D D D A A A A B C C C C C C B B B pattern space for A B C D That is the two classes (A,B) (C,D) are linearly separable, as too are the classes (A,D) and (B,C). Neural Nets: 3 3 We may now train two units (with outputs y 1 ; y 2 ) to perform these two classifications 1 0 y 1 y 2 (A B) (C D) (A D) (B C) y1 y2 classification This gives a table encoding the original 4 classes 1 1 1 1 0 0 0 0 y 1 y 2 Class C D B A y1 y2 coding for A B C D
Penetration Testing with Improved Input Vector Identification
 IN: PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON SOFTWARE TESTING (ICST
, 2009
"... Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts of penetra ..."
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Cited by 9 (2 self)
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Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts
Exact and Heuristic Approaches to Input Vector Control for
"... We present two approaches to leakage power minimization in static CMOC circuits by means of input vector control (IVC). We model leakage effects using pseudoBoolean functions. These are incorporated into an optimal integer linear programming model called VGILP that analyzes leakage variation with ..."
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We present two approaches to leakage power minimization in static CMOC circuits by means of input vector control (IVC). We model leakage effects using pseudoBoolean functions. These are incorporated into an optimal integer linear programming model called VGILP that analyzes leakage variation
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vec ..."
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Cited by 958 (5 self)
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vector machine' (RVM), a model of identical functional form to the popular and stateoftheart `support vector machine' (SVM). We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer
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 728 (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
LIBSVM: a Library for Support Vector Machines
, 2001
"... LIBSVM is a library for support vector machines (SVM). Its goal is to help users can easily use SVM as a tool. In this document, we present all its implementation details. 1 ..."
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Cited by 6287 (82 self)
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LIBSVM is a library for support vector machines (SVM). Its goal is to help users can easily use SVM as a tool. In this document, we present all its implementation details. 1
Adhoc OnDemand Distance Vector Routing
 IN PROCEEDINGS OF THE 2ND IEEE WORKSHOP ON MOBILE COMPUTING SYSTEMS AND APPLICATIONS
, 1997
"... An adhoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. In this paper we present Adhoc On Demand Distance Vector Routing (AODV), a novel algorithm for the operation of such adhoc n ..."
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Cited by 3167 (15 self)
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An adhoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. In this paper we present Adhoc On Demand Distance Vector Routing (AODV), a novel algorithm for the operation of such ad
Fast texture synthesis using treestructured vector quantization
, 2000
"... Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given ..."
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Cited by 562 (12 self)
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Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given
Results 1  10
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814,062