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209
A genetic distance metric to discriminate the selection of algorithms for the general ATSP problem
, 2010
"... Abstract. The only metric that had existed so far to determine the best algorithm for solving an general Asymmetric Traveling Salesman Problem (ATSP) instance is based on the number of cities; nevertheless, it is not sufficiently adequate for discriminating the best algorithm for solving an ATSP in ..."
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instance, thus the necessity for devising a new metric through the use of datamining techniques. In this paper we propose: (1) the use of a genetic distance metric for improving the selection of the algorithms that best solve a given instance of the ATSP and (2) the use of discriminant analysis as a means
Dynamic programming algorithm optimization for spoken word recognition
 IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
, 1978
"... This paper reports on an optimum dynamic programming (DP) based timenormalization algorithm for spoken word recognition. First, a general principle of timenormalization is given using timewarping function. Then, two timenormalized distance definitions, ded symmetric and asymmetric forms, are der ..."
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Cited by 788 (3 self)
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This paper reports on an optimum dynamic programming (DP) based timenormalization algorithm for spoken word recognition. First, a general principle of timenormalization is given using timewarping function. Then, two timenormalized distance definitions, ded symmetric and asymmetric forms
Dimensionality Reduction Using Genetic Algorithms
, 2000
"... Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern has a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving ..."
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Cited by 140 (11 self)
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approach to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil
DiscriminantEM Algorithm with Application to Image Retrieval
, 2000
"... In many vision applications, the practice of supervised learning faces several difficulties, one of which is that insufficient labeled training data result in poor generalization. In image retrieval, we have very few labeled images from query and relevance feedback so that it is hard to automaticall ..."
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Cited by 70 (4 self)
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retrieval as a transductive learning problem, in which the generalization of an image classifier is only defined on a set of images such as the given image database. Formulating this transductive problem in a probabilistic framework, the proposed algorithm, DiscriminantEM (DEM), not only estimates
Bayesian active distance metric learning
 UAI
, 2007
"... Distance metric learning is an important component for many tasks, such as statistical classification and contentbased image retrieval. Existing approaches for learning distance metrics from pairwise constraints typically suffer from two major problems. First, most algorithms only offer point estim ..."
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Cited by 16 (2 self)
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estimation of the distance metric and can therefore be unreliable when the number of training examples is small. Second, since these algorithms generally select their training examples at random, they can be inefficient if labeling effort is limited. This paper presents a Bayesian framework for distance
The Racing Algorithm: Model Selection for Lazy Learners
 Artificial Intelligence Review
, 1997
"... Given a set of models and some training data, we would like to find the model that best describes the data. Finding the model with the lowest generalization error is a computationally expensive process, especially if the number of testing points is high or if the number of models is large. Optimizat ..."
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Cited by 68 (2 self)
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. Optimization techniques such as hill climbing or genetic algorithms are helpful but can end up with a model that is arbitrarily worse than the best one or cannot be used because there is no distance metric on the space of discrete models. In this paper we develop a technique called "
Genetic algorithms for feature selection and weighting
 in: Proceedings of the IJCAI’99 workshop on Automating the Construction of Case Based Reasoners
, 1999
"... Abstract Automated techniques to optimise the retrieval of relevant cases in a CBR system are desirable as a way to reduce the expensive knowledge acquisition phase. This paper concentrates on feature selection methods that assist in indexing the casebase, and feature weighting methods that improv ..."
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Cited by 10 (0 self)
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in the casebase, as the index. However, induction algorithms like C4.5 apply a greedy selection approach and so the features used by the index are not always the optimal ones. This is a particular problem when the cases contain many features irrelevant to the problem solving The cases identified
Robust and Discriminative Distance for MultiInstance Learning
"... MultiInstance Learning (MIL) is an emerging topic in machine learning, which has broad applications in computer vision. For example, by considering video classification as a MIL problem where we only need labeled video clips (such as tagged online videos) but not labeled video frames, one can low ..."
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Cited by 3 (0 self)
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lower down the labeling cost, which is typically very expensive. We propose a novel class specific distance Metrics enhanced ClasstoBag distance (MC2B) method to learn a robust and discriminative distance for multiinstance data, which employs the notsquared ℓ2norm distance to address the most
A General Method for Handling Constraints in Genetic Algorithms
 In Proceedings of the Second Annual Joint Conference on Information Science
, 1995
"... This paper explores the development of a generalized constraint handling method that does not require parameter tuning. Within engineering design problems, constraints can be classified as explicit or implicit. Explicit constraints can be checked without requiring any type of simulation. For example ..."
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Cited by 9 (0 self)
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This paper explores the development of a generalized constraint handling method that does not require parameter tuning. Within engineering design problems, constraints can be classified as explicit or implicit. Explicit constraints can be checked without requiring any type of simulation
Results 1  10
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209