Results 221 to 230 of about 223,210 (262)

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
openaire   +1 more source

Supervised Classification of Geriatric Anxiety

Proceedings of the 2019 4th International Conference on Intelligent Information Technology, 2019
Anxiety is a common symptom in elderly people and is associated with dementia. In this study, we apply the machine learning methods to classify anxiety patients based on GAI. We confirm the possibility of reducing the number of GAI questionnaires, which is to make GAI testing easier for the elderly.
Jae-Kyeong Sim, Hyungtai Kim, Geon Ha Kim, Mun-Taek Choi   +3 more
openaire   +1 more source

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
openaire   +1 more source

Supervised Classification of Metabolic Networks

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018
Networks represent a convenient model for many scientific and technological problems. From power grids to biological processes and functions, from financial networks to chemical compounds, the representation of case studies with graphs enables the possibility to highlight both topological and qualitative characteristics. In this work, we are interested
Ilaria Granata, Mario R. Guarracino, Valeriy A. Kalyagin, Lucia Maddalena 0001, Ichcha Manipur, Panos M. Pardalos   +5 more
openaire   +4 more sources

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
openaire   +1 more source

Supervised Classification

2023
Juan J. Cuadrado-Gallego, Yuri Demchenko
  +4 more sources

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.
openaire   +2 more sources

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
openaire   +1 more source

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.
openaire   +1 more source

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
openaire   +1 more source