The Experts below are selected from a list of 5214 Experts worldwide ranked by ideXlab platform
Leon Bobrowski - One of the best experts on this subject based on the ideXlab platform.
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Induction of Linear Separability Through the Ranked Layers of Binary Classifiers
2020Co-Authors: Leon BobrowskiAbstract:Abstract: The concept of Linear Separability is used in the theory of neural networks and pattern recognition methods. This term can be related to examination of learning sets (classes) separation by hyperplanes in a given feature space. The family of K disjoined learning sets can be transformed into K Linearly separable sets by the ranked layer of binary classifiers. Problems of the ranked layers deigning are analyzed in the paper. Keywords. Learning sets, Linear Separability, formal neurons, binary classifiers, ranked Introduction The Perceptron model and the error-correction learning algorithm of formal neurons played a fundamental role in the early neural networks [1], The most popular algorithms currently used in data mining are the support vector machines (SVM
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selection of genetic and phenotypic features associated with inflammatory status of patients on dialysis using relaxed Linear Separability method
PLOS ONE, 2014Co-Authors: Leon Bobrowski, Tomasz łukaszuk, Bengt Lindholm, Peter Stenvinkel, Olof Heimburger, Jonas Axelsson, Peter Barany, Juan Jesus Carrero, Abdul Rashid Qureshi, Karin LuttroppAbstract:Identification of risk factors in patients with a particular disease can be analyzed in clinical data sets by using feature selection procedures of pattern recognition and data mining methods. The applicability of the relaxed Linear Separability (RLS) method of feature subset selection was checked for high-dimensional and mixed type (genetic and phenotypic) clinical data of patients with end-stage renal disease. The RLS method allowed for substantial reduction of the dimensionality through omitting redundant features while maintaining the Linear Separability of data sets of patients with high and low levels of an inflammatory biomarker. The synergy between genetic and phenotypic features in differentiation between these two subgroups was demonstrated.
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cpl criterion functions and learning algorithms linked to the Linear Separability concept
International Conference on Engineering Applications of Neural Networks, 2013Co-Authors: Leon BobrowskiAbstract:Linear separabilty of learning sets is a basic concept of neural networks theory. Exploration of the Linear Separability can be based on the minimization of the perceptron criterion function. Modification of the perceptron criterion function have been proposed recently aimed at feature selection problem. The modified criterion functions allows, among others, for discovering minimal feature subset that assure Linear Separability. Learning algorithm linked to the modified function is formulated in the paper.
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relaxed Linear Separability rls approach to feature gene subset selection
2011Co-Authors: Leon Bobrowski, Tomasz łukaszukAbstract:Feature selection is one of active research area in pattern recognition or data mining methods (Duda et al., 2001). The importance of feature selection methods becomes apparent in the context of rapidly growing amount of data collected in contemporary databases (Liu & Motoda, 2008). Feature subset selection procedures are aimed at neglecting as large as possible number of such features (measurements) which are irrelevant or redundant for a given problem. The feature subset resulting from feature selection procedure should allow to build a model on the base of available learning data sets that generalizes better to new (unseen) data. For the purpose of designing classification or prediction models, the feature subset selection procedures are expected to produce higher classification or prediction accuracy. Feature selection problem is particularly important and challenging in the case when the number of objects represented in a given database is low in comparison to the number of features which have been used to characterise these objects. Such situation appears typically in exploration of genomic data sets where the number of features can be thousands of times greater than the number of objects. Here we are considering the relaxed Linear Separability (RLS) method of feature subset selection (Bobrowski & Łukaszuk, 2009). Such approach to feature selection problem refers to the concept of Linear Separability of the learning sets (Bobrowski, 2008). The term “relaxation” means here deterioration of the Linear Separability due to the gradual neglect of selected features. The considered approach to feature selection is based on repetitive minimization of the convex and piecewise-Linear (CPL) criterion functions. These CPL criterion functions, which have origins in the theory of neural networks, include the cost of various features (Bobrowski, 2005). Increasing the cost of individual features makes these features falling out of the feature subspace. Quality the reduced feature subspaces is assessed by the accuracy of the CPL optimal classifiers built in this subspace. The article contains a new theoretical and experimental results related to the RLS method of feature subset selection. The experimental results have been achieved through the analysis, inter alia, two sets of genetic data.
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Induction of Linear Separability through the Ranked Layers of Binary Classifiers
2011Co-Authors: Leon BobrowskiAbstract:The concept of Linear Separability is used in the theory of neural networks and pattern recognition methods. This term can be related to examination of learning sets (classes) separation by hyperplanes in a given feature space. The family of K disjoined learning sets can be transformed into K Linearly separable sets by the ranked layer of binary classifiers. Problems of the ranked layers deigning are analyzed in the paper.
David Elizondo - One of the best experts on this subject based on the ideXlab platform.
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A near Linear algorithm for testing Linear Separability in two dimensions
2012Co-Authors: Sylvain Contassot-vivier, David ElizondoAbstract:We present a near Linear algorithm for determining the Linear Separability of two sets of points in a two-dimensional space. That algorithm does not only detects the Linear Separability but also computes separation information. When the sets are Linearly separable, the algorithm provides a description of a separation hyperplane. For non Linearly separable cases, the algorithm indicates a negative answer and provides a hyperplane of partial separation that could be useful in the building of some classification systems.
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Linear Separability and classification complexity
Expert Systems With Applications, 2012Co-Authors: David Elizondo, Ralph Birkenhead, Matias Gamez, Noelia Garcia, Esteban AlfaroAbstract:We study the relationship between Linear Separability and the level of complexity of classification data sets. Linearly separable classification problems are generally easier to solve than non Linearly separable ones. This suggests a strong correlation between Linear Separability and classification complexity. We propose a novel and simple method for quantifying the complexity of the classification problem. The method, which is shown below, reduces any two class classification problem to a sequence of Linearly separable steps. The number of such reduction steps could be viewed as measuring the degree of non-Separability and hence the complexity of the problem. This quantification in turn can be used as a measure for the complexity of classification data sets. Results obtained using several benchmarks are provided.
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choice effect of Linear Separability testing methods on constructive neural network algorithms an empirical study
Expert Systems With Applications, 2011Co-Authors: David Elizondo, Juan Miguel Ortizdelazcanolobato, Ralph BirkenheadAbstract:Several algorithms exist for testing Linear Separability. The choice of a particular testing algorithm has effects on the performance of constructive neural network algorithms that are based on the transformation of a nonLinear Separability classification problem into a Linearly separable one. This paper presents an empirical study of these effects in terms of the topology size, the convergence time, and generalisation level of the neural networks. Six different methods for testing Linear Separability were used in this study. Four out of the six methods are exact methods and the remaining two are approximative ones. A total of nine machine learning benchmarks were used for this study.
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a novel and efficient method for testing non Linear Separability
International Conference on Artificial Neural Networks, 2007Co-Authors: David Elizondo, Juan Miguel Ortizdelazcanolobato, Ralph BirkenheadAbstract:The notion of Linear Separability is widely used in machine learning research. Learning algorithms that use this concept to learn include neural networks (Single Layer Perceptron and Recursive Deterministic Perceptron), and kernel machines (Support Vector Machines). Several algorithms for testing Linear Separability exist. Some of these methods are computationally intense. Also, several of them will converge if the classes are Linearly separable, but will fail to converge otherwise. A fast and efficient test for non Linear Separability is proposed which can be used to pretest classification data sets for non Linear Separability thus avoiding expensive computations. This test is based on the convex hull Separability method but does not require the computation of the convex hull.
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the Linear Separability problem some testing methods
IEEE Transactions on Neural Networks, 2006Co-Authors: David ElizondoAbstract:The notion of Linear Separability is used widely in machine learning research. Learning algorithms that use this concept to learn include neural networks (single layer perceptron and recursive deterministic perceptron), and kernel machines (support vector machines). This paper presents an overview of several of the methods for testing Linear Separability between two classes. The methods are divided into four groups: Those based on Linear programming, those based on computational geometry, one based on neural networks, and one based on quadratic programming. The Fisher Linear discriminant method is also presented. A section on the quantification of the complexity of classification problems is included.
David Zhang - One of the best experts on this subject based on the ideXlab platform.
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a face and palmprint recognition approach based on discriminant dct feature extraction
Systems Man and Cybernetics, 2004Co-Authors: Xiaoyuan Jing, David ZhangAbstract:In the field of image processing and recognition, discrete cosine transform (DCT) and Linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional Separability judgment to select the DCT frequency bands with favorable Linear Separability. Then from the selected bands, it extracts the Linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art Linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.
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a training approach based on Linear Separability analysis for layered perceptrons
World Congress on Computational Intelligence, 1994Co-Authors: David Zhang, Mohamed S Kamel, M I ElmasryAbstract:In this paper, we explore Linear Separability as a training approach for layered perceptrons. A training approach, called layer adaptation (LA), is presented. Its learning mechanism and implementation are described and examples are given to illustrate its effectiveness. Compared with the BP and the MRII algorithms, preliminary analysis shows that the LA is easily implemented using digital VLSI technology while the stability, the training time and the complexity in silicon are acceptable. >
Donald Homa - One of the best experts on this subject based on the ideXlab platform.
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expanding the search for a Linear Separability constraint on category learning
Memory & Cognition, 2001Co-Authors: Mark R Blair, Donald HomaAbstract:Formal models of categorization make different predictions about the theoretical importance of Linear Separability. Prior research, most of which has failed to find support for a Linear Separability constraint on category learning, has been conducted using tasks that involve learning two categories with a small number of members. The present experiment used four categories with three or nine patterns per category that were either Linearly separable or not Linearly separable. With overall category structure equivalent across category types, the Linearly separable categories were found to be easier to learn than the not Linearly separable categories. An analysis of individual participants’ data showed that there were more participants operating under a Linear Separability constraint when learning large categories than when learning small ones. Formal modeling showed that an exemplar model could not account for many of these data. These results are taken to support the existence of multiple processes in categorization.
Ralph Birkenhead - One of the best experts on this subject based on the ideXlab platform.
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Linear Separability and classification complexity
Expert Systems With Applications, 2012Co-Authors: David Elizondo, Ralph Birkenhead, Matias Gamez, Noelia Garcia, Esteban AlfaroAbstract:We study the relationship between Linear Separability and the level of complexity of classification data sets. Linearly separable classification problems are generally easier to solve than non Linearly separable ones. This suggests a strong correlation between Linear Separability and classification complexity. We propose a novel and simple method for quantifying the complexity of the classification problem. The method, which is shown below, reduces any two class classification problem to a sequence of Linearly separable steps. The number of such reduction steps could be viewed as measuring the degree of non-Separability and hence the complexity of the problem. This quantification in turn can be used as a measure for the complexity of classification data sets. Results obtained using several benchmarks are provided.
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choice effect of Linear Separability testing methods on constructive neural network algorithms an empirical study
Expert Systems With Applications, 2011Co-Authors: David Elizondo, Juan Miguel Ortizdelazcanolobato, Ralph BirkenheadAbstract:Several algorithms exist for testing Linear Separability. The choice of a particular testing algorithm has effects on the performance of constructive neural network algorithms that are based on the transformation of a nonLinear Separability classification problem into a Linearly separable one. This paper presents an empirical study of these effects in terms of the topology size, the convergence time, and generalisation level of the neural networks. Six different methods for testing Linear Separability were used in this study. Four out of the six methods are exact methods and the remaining two are approximative ones. A total of nine machine learning benchmarks were used for this study.
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a novel and efficient method for testing non Linear Separability
International Conference on Artificial Neural Networks, 2007Co-Authors: David Elizondo, Juan Miguel Ortizdelazcanolobato, Ralph BirkenheadAbstract:The notion of Linear Separability is widely used in machine learning research. Learning algorithms that use this concept to learn include neural networks (Single Layer Perceptron and Recursive Deterministic Perceptron), and kernel machines (Support Vector Machines). Several algorithms for testing Linear Separability exist. Some of these methods are computationally intense. Also, several of them will converge if the classes are Linearly separable, but will fail to converge otherwise. A fast and efficient test for non Linear Separability is proposed which can be used to pretest classification data sets for non Linear Separability thus avoiding expensive computations. This test is based on the convex hull Separability method but does not require the computation of the convex hull.