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Algorithm Details Examples Summary
"... The confirmation of a suspected structure Elucidation: The determination of an unknown structure Both start with spectra, but have very different workflows ..."
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The confirmation of a suspected structure Elucidation: The determination of an unknown structure Both start with spectra, but have very different workflows
eCCCBiclustering: Algorithmic details
"... This document provides supplementary material describing algorithmic and complexity details of eCCCBiclustering. For clarity we repeat here the details of the main steps of eCCCBiclustering already presented in the main manuscript. We believe that a complete description of the algorithmic detail ..."
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This document provides supplementary material describing algorithmic and complexity details of eCCCBiclustering. For clarity we repeat here the details of the main steps of eCCCBiclustering already presented in the main manuscript. We believe that a complete description of the algorithmic
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
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Cited by 2213 (20 self)
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In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced
A comparative analysis of selection schemes used in genetic algorithms
 Foundations of Genetic Algorithms
, 1991
"... This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, ranking selection, tournament selection, and Genitor (or «steady state") selection are compared on the basis of solutions to deterministic difference or d ..."
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Cited by 531 (31 self)
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This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, ranking selection, tournament selection, and Genitor (or «steady state") selection are compared on the basis of solutions to deterministic difference
A comparison and evaluation of multiview stereo reconstruction algorithms.
 In Proc. Computer Vision and Pattern Recognition ’06,
, 2006
"... Abstract This paper presents a quantitative comparison of several multiview stereo reconstruction algorithms. Until now, the lack of suitable calibrated multiview image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multiv ..."
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Cited by 530 (14 self)
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quantitative comparison of stateoftheart multiview stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://vision.middlebury.edu/mview.
Marching cubes: A high resolution 3D surface construction algorithm
 COMPUTER GRAPHICS
, 1987
"... We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divideandconquer approach to generate interslice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical d ..."
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Cited by 2696 (4 self)
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We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divideandconquer approach to generate interslice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical
On the algorithmic implementation of multiclass kernelbased vector machines
 Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernelbased vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
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Cited by 559 (13 self)
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to incorporate kernels with a compact set of constraints and decompose the dual problem into multiple optimization problems of reduced size. We describe an efficient fixedpoint algorithm for solving the reduced optimization problems and prove its convergence. We then discuss technical details that yield
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
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Cited by 693 (4 self)
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) finding the parameters of a hidden Markov model (HMM) (i.e., the BaumWelch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical
1. The Algorithmic Details of Tractable MFC
"... In this section, we first present the algorithm that infers the most probable tree T ∗ i from the generated foreground candidates Bi. Then, we discuss the dynamic programming based search algorithm to obtain the optimal solution to Eq.(1) in the main draft. Refer to Section 3.3 of the main draft for ..."
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In this section, we first present the algorithm that infers the most probable tree T ∗ i from the generated foreground candidates Bi. Then, we discuss the dynamic programming based search algorithm to obtain the optimal solution to Eq.(1) in the main draft. Refer to Section 3.3 of the main draft
Supporting Text 1. Algorithm Details
"... Consider a corpus of m sentences (sequences) of variable length, each expressed in terms of a lexicon of finite size N. The sentences in the corpus correspond to m different paths in a pseudograph (a nonsimple graph in which both loops and multiple edges are permitted) whose vertices are the unique ..."
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Consider a corpus of m sentences (sequences) of variable length, each expressed in terms of a lexicon of finite size N. The sentences in the corpus correspond to m different paths in a pseudograph (a nonsimple graph in which both loops and multiple edges are permitted) whose vertices are the unique lexicon entries, augmented by two special symbols, begin and end. Each of the N nodes has a number of incoming paths that is equal to the number of outgoing paths. Fig. 4 illustrates the type of structure that we seek, namely, the bundling of paths, signifying a relatively high probability associated with a substructure that can be identified as a pattern. To extract it from the data, two probability functions are defined over the graph for any given search path S = (e1 → e2 → · · · → ek) = (e1; ek). ∗ The first one, PR(ei; ej), is the rightmoving ratio of fanthrough flux of paths at ej to fanin flux of paths at ej−1, starting at ei and moving along the subpath ei → ei+1 → ei+2 · · · → ej−1 PR(ei; ej) = p(ejeiei+1ei+2...ej−1) = l(ei; ej), [1] l(ei; ej−1) where l(ei; ej) is the number of occurrences of subpaths (ei; ej) in the graph. Proceeding in the opposite direction, from the right end of the path to the left, we define the leftgoing probability function PL and note that
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