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Local linear perceptrons for classification
1996
IEEE Transactions on Neural Networks
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Abstract|A structure composed of local linear perceptrons for approximating global class discriminants is investigated. Such local linear models may be combined in a cooperative or competitive way. ...
Learning of the local models' positions and the linear mappings they implement are coupled and both supervised. ...
We thank Zoubin Ghahramani and Rod Murray-Smith for stimulating discussions. ...
doi:10.1109/72.501737
pmid:18263476
fatcat:hyegu2s5oncmlpcmuhmebg4zea
Dimensionality Reduction and Microarray Data
[chapter]
2008
Lecture Notes in Computational Science and Engineering
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This article presents a comparison study of the performance of the linear principal component analysis and the non linear local tangent space alignment principal manifold methods on such a problem. ...
Microarrays are being currently used for the expression levels of thousands of genes simultaneously. ...
Therefore, more data was available to train the perceptron linear classification models. ...
doi:10.1007/978-3-540-73750-6_13
fatcat:7yej2do2z5hafd6fddijss3vca
Local Supervised Learning through Space Partitioning
2012
Neural Information Processing Systems
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We train locally linear classifiers by using LDA, logistic regression and perceptrons, and so our scheme is scalable to large data sizes and high-dimensions. ...
Nevertheless, we consider locally linear schemes by learning linear partitions and linear region classifiers. ...
Local linear classifiers were trained with LDA, logistic regression, and perceptron (mean of weights) used to learn local surrogates for the rejection and local classification problems. ...
dblp:conf/nips/WangS12
fatcat:sj4qv3aqdfanlm5zanwjfx4hgi
Incorporating linear discriminant analysis in neural tree for multidimensional splitting
2013
Applied Soft Computing
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The proposed hybrid classifier, neural tree with linear discriminant analysis called NTLD, adopts a tree structure containing either a simple perceptron or a linear discriminant at each node. ...
The weakly performing perceptron nodes are replaced with DF-LDA in an automatic way. ...
The second author gratefully acknowledges the support of IIT Roorkee for carrying out this work. ...
doi:10.1016/j.asoc.2013.06.007
fatcat:vtlig7ut4rebxahswqeism4xea
Linear Versus Nonlinear Neural Modeling for 2-D Pattern Recognition
2005
IEEE transactions on systems, man and cybernetics. Part A. Systems and humans
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Using a genetic algorithm, linear and nonlinear inputs to the linear classifier are selected to improve classification performance. ...
Results show that an appropriate set of linear and nonlinear inputs to the linear classifier were selected, improving significantly its classification performance in both problems. ...
Salinas for his initial contribution to this work. The authors would like to thank the reviewers for their comments. ...
doi:10.1109/tsmca.2005.851268
fatcat:uy2kze54n5gfhpkf56g5t4uw4m
A Novel Extreme Learning Machine Based on Hybrid Kernel Function
2013
Journal of Computers
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Compared with traditional ELM, the results show that this method can effectively improve the ELM classification results, avoid local minimum, with better generalization, robustness, controllability and ...
Extreme learning machine is a new learning algorithm for the single hidden layer feedforward neural networks (SLFNs). ...
But for wine dataset, the RBF kernel function classification effect is better than the Perceptron kernel function. ...
doi:10.4304/jcp.8.8.2110-2117
fatcat:3qvmvddwora6xi72765dug4rgu
A Novel Iterative Linear Regression Perceptron Classifier for Breast Cancer Prediction
2017
International Journal of Computer Applications
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This work is an extension of earlier implementation of breast cancer analysis of the author through iterative linear regressive classifier. ...
Breast cancer, the most common of types of cancer that threatens human life more specifically women can be diagnosed with classification techniques of data mining. ...
The authors have overcome the local optima issue of neural network differential evolution algorithm for determining the optimal value or near optimal value for ANN parameters. ...
doi:10.5120/ijca2017914461
fatcat:3jlygg6m5ve5rlmktsjzheswz4
Linear Dilation-Erosion Perceptron Trained Using a Convex-Concave Procedure
[article]
2020
arXiv
pre-print
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In this paper, we present the linear dilation-erosion perceptron (ℓ-DEP), which is given by applying linear transformations before computing a dilation and an erosion. ...
Mathematical morphology (MM) is a theory of non-linear operators used for the processing and analysis of images. ...
First of all, it is widely know that the perceptron, introduced by Rosenblatt in the late 1950s, can be used for binary classification tasks [18] . ...
arXiv:2011.06512v1
fatcat:6pwvzhhmc5gafczc4qwvppwv7m
Discriminative Binaural Sound Localization
2002
Neural Information Processing Systems
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However, linear classifiers may not suffice to obtain in many applications, including the sound localization application. We therefore incorporate kernels into the multiclass Perceptron. ...
Online Learning using Multiclass Perceptron with Kernels: Despite, or because of, its age the Perceptron algorithm [9] is a simple and effective algorithm for classification. ...
dblp:conf/nips/Ben-ReuvenS02
fatcat:yyluhpzirjapxeu5oeex6jl4ia
A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers
[article]
2015
arXiv
pre-print
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We show that using the dynamic selection of linear classifiers through the META-DES framework, we can solve complex non-linear classification problems where other combination techniques such as AdaBoost ...
Moreover, an analysis of the impact of several factors in the system performance, such as the number of classifiers in the pool, the use of different linear base classifiers, as well as the size of the ...
Two linear classifiers trained for this problem (two Perceptrons) c 1 and c 2 , both with an individual accuracy of 50%. ...
arXiv:1509.00825v2
fatcat:l6pdsilmxjcebfcsmuq3hvfqyi
A SURVEY ON CLASSIFICATION TECHNIQUES USED FOR RAINFALL FORECASTING
2017
International Journal of Advanced Research in Computer Science
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In this paper, we discuss different classification techniques used for rainfall forecasting. ...
A variety of classification techniques such as Decision Tree Induction, Bayesian Classification, Naïve Bayes Classifiers, Artificial Neural Networks, Multi Layer Perceptron, Genetic algorithms , Fuzzy ...
MULTILAYER PERCEPTRON The Multi Layer Perceptron (MLP) or Feed-forward network is a type of artificial neural network that consists of a non linear activation function in hidden layer. ...
doi:10.26483/ijarcs.v8i8.4645
fatcat:lzchp6qwsvbqpgmn7knkmix7pu
Enhanced perceptrons using contrastive biclusters
[article]
2016
arXiv
pre-print
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Upon each local subspace associated with a pair of contrastive biclusters a perceptron is trained and the model with highest area under the receiver operating characteristic curve (AUC) value is selected ...
Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. ...
linear classification models. ...
arXiv:1603.06859v1
fatcat:3mins5akprbbln74cw4nwxyyyq
An Algorithm for Building Regularized Piecewise Linear Discrimination Surfaces: The Perceptron Membrane
1995
Neural Computation
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Péri, 92 245 Malakoff, France
The perceptron membrane is a new connectionist model that aims at solving discrimination (classification) problems with piecewise linear surfaces. ...
algorithm because of numerous possibilities for local minima. ...
doi:10.1162/neco.1995.7.2.380
fatcat:v6yepuqbyjddvb2qxycpp2usnq
ICA as a Preprocessing Technique for Classification
[chapter]
2004
Lecture Notes in Computer Science
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In this paper we propose the use of the independent component analysis (ICA) [1] technique for improving the classification rate of decision trees and multilayer perceptrons [2], [3] . ...
The use of an ICA for the preprocessing stage, makes the structure of both classifiers simpler, and therefore improves the generalization properties. ...
For instance, in the classification task for the echocardiogram database, we have a set of variables, which although are related, are not generated by a linear mixture. ...
doi:10.1007/978-3-540-30110-3_147
fatcat:x2rgshwaijfi3ohp46l7mhwyeu
Online Learning via Global Feedback for Phrase Recognition
2003
Neural Information Processing Systems
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We provide a recognition-based feedback rule which reflects to each local function its committed errors from a global point of view, and allows to train them together online as perceptrons. ...
This work presents an architecture based on perceptrons to recognize phrase structures, and an online learning algorithm to train the perceptrons together and dependently. ...
the linear case and no improvements for degrees > 2. ...
dblp:conf/nips/CarrerasM03
fatcat:adpx4eppsfegbirkls76rznhhi
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