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1,595
Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
, 1995
"... A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in k-d space, using k feature-extraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly fine-tuned spatial access methods (SAMs), to answer several ..."
Abstract
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Cited by 502 (22 self)
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A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in k-d space, using k feature-extraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly fine-tuned spatial access methods (SAMs), to answer several
Max-margin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
Abstract
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Cited by 604 (15 self)
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. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M 3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm
A Direct Evolutionary Feature Extraction Algorithm for Classifying
- High Dimensional Data,” American Association for Artificial Intelligence 2006
, 2006
"... Among various feature extraction algorithms, those based on genetic algorithms are promising owing to their potential parallelizability and possible applications in large scale and high dimensional data classification. However, existing genetic algorithm based feature ex-traction algorithms are eith ..."
Abstract
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Cited by 2 (2 self)
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are either limited in searching opti-mal projection basis vectors or costly in both time and space complexities and thus not directly applicable to high dimensional data. In this paper, a direct evolution-ary feature extraction algorithm is proposed for classify-ing high-dimensional data. It constructs
Using mutual information for selecting features in supervised neural net learning
- IEEE TRANSACTIONS ON NEURAL NETWORKS
, 1994
"... This paper investigates the application of the mutual infor“ criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is ..."
Abstract
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Cited by 358 (1 self)
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, the use of the mutual information for tasks characterized by high input dimensionality requires suitable approximations because of the prohibitive demands on computation and samples. An algorithm is proposed that is based on a “greedy” selection of the features and that takes both the mutual information
Feature Extraction for Dynamic Integration of Classifiers
- (submitted to) Fundamenta Informaticae
"... Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique ..."
Abstract
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Cited by 1 (0 self)
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Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based
Inferring Global Perceptual Contours from Local Features
, 1996
"... Introduction Computer vision can greatly benefit from perceptual grouping. Perceptual Grouping can be classified as a mid-level process directed toward closing the gap between what is produced by state-of-the-art low-level algorithms (such as edge detectors) and what is desired as input to high lev ..."
Abstract
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Cited by 210 (10 self)
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Introduction Computer vision can greatly benefit from perceptual grouping. Perceptual Grouping can be classified as a mid-level process directed toward closing the gap between what is produced by state-of-the-art low-level algorithms (such as edge detectors) and what is desired as input to high
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 ..."
Abstract
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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
Sparse multinomial logistic regression: fast algorithms and generalization bounds
- IEEE Trans. on Pattern Analysis and Machine Intelligence
"... Abstract—Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly larg ..."
Abstract
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Cited by 190 (1 self)
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and the feature dimensionality, making them applicable even to large data sets in high-dimensional feature spaces. To the best of our knowledge, these are the first algorithms to perform exact multinomial logistic regression with a sparsity-promoting prior. Third, we show how nontrivial generalization bounds can
A survey of evolutionary algorithms for data mining and knowledge discovery
- In: A. Ghosh, and S. Tsutsui (Eds.) Advances in Evolutionary Computation
, 2002
"... Abstract: This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowled ..."
Abstract
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Cited by 123 (3 self)
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of the knowledge discovery process, focusing on attribute selection and pruning of an ensemble of classifiers. We show how the requirements of data mining and knowledge discovery influence the design of evolutionary algorithms. In particular, we discuss how individual representation, genetic operators and fitness
Feature selection for classifying high-dimensional numerical data
- in: Proceedings of IEEE Conference on Computer Society, CVPR
"... Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse of dimensionality, which degrades both classification accuracy and efficiency. To address this issue, we present an effi ..."
Abstract
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Cited by 13 (3 self)
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Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse of dimensionality, which degrades both classification accuracy and efficiency. To address this issue, we present
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