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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
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 8 (3 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
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 561 (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
Decoding by Linear Programming
, 2004
"... This paper considers the classical error correcting problem which is frequently discussed in coding theory. We wish to recover an input vector f ∈ Rn from corrupted measurements y = Af + e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible to rec ..."
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Cited by 1399 (16 self)
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This paper considers the classical error correcting problem which is frequently discussed in coding theory. We wish to recover an input vector f ∈ Rn from corrupted measurements y = Af + e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible
Support vector machine learning for interdependent and structured output spaces
 In ICML
, 2004
"... Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernelbased methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs suchas multiple depe ..."
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Cited by 450 (20 self)
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dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits
Generation of Minimal Leakage Input Vectors with Constrained NBTI Degradation
"... Instability (NBTI) to emerge as a major circuit reliability concern. Simultaneously leakage power is becoming a greater fraction of the total power dissipated by logic circuits. As both NBTI and leakage power are highly dependent on vectors applied at the circuit’s inputs, they can be minimized by a ..."
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Cited by 1 (0 self)
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Instability (NBTI) to emerge as a major circuit reliability concern. Simultaneously leakage power is becoming a greater fraction of the total power dissipated by logic circuits. As both NBTI and leakage power are highly dependent on vectors applied at the circuit’s inputs, they can be minimized
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 783 (29 self)
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of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a preliminary theoretical analysis of the statistical performance of our
quadratic drift and constant controlinput vector fields
, 2012
"... Local controllability of controlaffine systems with ..."
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