Results 291 to 300 of about 5,782,720 (331)

Supervised radar signal classification

2016 International Joint Conference on Neural Networks (IJCNN), 2016
This work investigates radar signal classification and source identification using three classification models: Neural Networks (NN), Support Vector Machines (SVM) and Random Forests (RF). The available large dataset consists of pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan ...
Ivan Jordanov, Nedyalko Petrov, Alessio Petrozziello   +2 more
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Semi-supervised classification trees

Journal of Intelligent Information Systems, 2017
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and ...
Jurica Levatic, Michelangelo Ceci, Dragi Kocev, Saso Dzeroski   +3 more
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Maximizing Correlation for Supervised Classification

2007 15th International Conference on Digital Signal Processing, 2007
In this paper, we develop a novel feature selection and classification approach using the correlation maximization paradigm. This approach is particularly interesting when the number of features is very large in comparison to the number of samples, as in the datasets arising in the bioinformatics applications.
Kaushik Mahata, Pritha Mahata
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Regression and Classification in Supervised Learning

Proceedings of the 2nd International Conference on Computing and Big Data, 2019
The problem of recognizing patterns from big data has attracted a lot of attention these days, especially in artificial intelligence and machine learning fields. People are interested in training computers to make predictions or classifications on their own based on past experience, i.e., data.
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Classification of Countertransference for Utilization in Supervision

American Journal of Psychotherapy, 1983
Supervision of countertransference has been problematic for a number of reasons. Countertransference has had many different meanings and negative affective connotations associated with it. Conflict has existed over whether it is a subject more appropriate to personal therapy than to supervision.
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Modeling Dependencies in Supervised Classification

2017
In this paper we show the advantage of modeling dependencies in supervised classification. The dependencies among variables in a multivariate data set can be linear or non linear. For this reason, it is important to consider flexible tools for modeling such dependencies. Copula functions are able to model different kinds of dependence structures. These
Rogelio Salinas-Gutiérrez, Angélica Hernández-Quintero, Oscar Dalmau Cedeño, Ángela Paulina Pérez-Díaz   +3 more
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Semi-Supervised Classification of Graph Convolutional Networks with Laplacian Rank Constraints

Neural Processing Letters, 2021
Hai-Qi Zhang, Guang-Quan Lu, Mengmeng Zhan, Beixian Zhang   +3 more
semanticscholar   +1 more source

Supervised Classification with Associative SOM

2003
We review a technique for creating Self-organising Maps (SOMs) in a Feature space which is nonlinearly related to the original data space. We show that convergence is remarkably fast for this method. The resulting map has two properties which are interesting from a biological perspective: first, the learning forms topology preserving mappings extremely
Rafael del-Hoyo-Alonso, J. David Buldain Pérez, Álvaro Marco   +2 more
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Supervised neighborhood graph construction for semi-supervised classification

Pattern Recognition, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mohammad H. Rohban, Hamid R. Rabiee 0001
openaire   +3 more sources

Proactive Forest for Supervised Classification

2018
Random Forest is one of the most used and accurate ensemble methods based on decision trees. Since diversity is a necessary condition to build a good ensemble, Random Forest selects a random feature subset for building decision nodes. This generation procedure could cause important features to be selected in multiple trees in the ensemble, decreasing ...
Nayma Cepero-Pérez, Luis Alberto Denis-Miranda, Rafael Hernández-Palacio, Mailyn Moreno-Espino, Milton García-Borroto   +4 more
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