scholarly journals An SVM-Based Framework for Long-Term Learning Systems

2019 ◽  
Vol 33 ◽  
pp. 9915-9916
Author(s):  
Diana Benavides-Prado
Keyword(s):  
Binary Classification ◽  
Upper Bounds ◽  
Support Vector ◽  
Transfer Of Knowledge ◽  
Learning Framework ◽  
Vector Machines ◽  
Novel Method ◽  
Classification Tasks ◽  
Retention Of Knowledge

In our research, we study the problem of learning a sequence of supervised tasks. This is a long-standing challenge in machine learning. Our work relies on transfer of knowledge between hypotheses learned with Support Vector Machines. Transfer occurs in two directions: forward and backward. We have proposed to selectively transfer forward support vector coefficients from previous hypotheses as upper-bounds on support vector coefficients to be learned on a target task. We also proposed a novel method for refining existing hypotheses by transferring backward knowledge from a target hypothesis learned recently. We have improved this method through a hypothesis refinement approach that refines whilst encouraging retention of knowledge. Our contribution is represented in a long-term learning framework for binary classification tasks received sequentially one at a time.

Comparing Classification Methods for Longitudinal fMRI Studies

2010 ◽  
Vol 22 (11) ◽  
pp. 2729-2762 ◽  
Author(s):  
Tanya Schmah ◽  
Grigori Yourganov ◽  
Richard S. Zemel ◽  
Geoffrey E. Hinton ◽  
Steven L. Small ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Binary Classification ◽  
Stroke Recovery ◽  
Support Vector ◽  
Linear Quadratic ◽  
Restricted Boltzmann Machines ◽  
K Nearest Neighbors ◽  
Vector Machines ◽  
Out Of Sample ◽  
Classification Tasks

We compare 10 methods of classifying fMRI volumes by applying them to data from a longitudinal study of stroke recovery: adaptive Fisher's linear and quadratic discriminant; gaussian naive Bayes; support vector machines with linear, quadratic, and radial basis function (RBF) kernels; logistic regression; two novel methods based on pairs of restricted Boltzmann machines (RBM); and K-nearest neighbors. All methods were tested on three binary classification tasks, and their out-of-sample classification accuracies are compared. The relative performance of the methods varies considerably across subjects and classification tasks. The best overall performers were adaptive quadratic discriminant, support vector machines with RBF kernels, and generatively trained pairs of RBMs.

scholarly journals Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1605
Author(s):  
Cesar Alfaro ◽  
Javier Gomez ◽  
Javier M. Moguerza ◽  
Javier Castillo ◽  
Jose I. Martinez
Keyword(s):  
Support Vector Machines ◽  
Data Exchange ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Training Set ◽  
Vector Machines ◽  
Comparable Performance ◽  
Novel Method ◽  
Classification Tasks

Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy consumption and minimizing data exchange between devices in different zones. This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs), based on ensembles, tailored to these kinds of problems. To achieve this, the training set is split into several Voronoi regions. These regions are small enough to permit faster parallel training of SVMs, reducing computational payload. Results from experiments comparing the proposed method with a single SVM and a standard ensemble of SVMs demonstrate that this approach can provide comparable performance while limiting the number of regions required to solve classification tasks. These advantages facilitate the development of energy-efficient policies in WSN.

Gaussian Processes for Classification: Mean-Field Algorithms

2000 ◽  
Vol 12 (11) ◽  
pp. 2655-2684 ◽  
Author(s):  
Manfred Opper ◽  
Ole Winther
Keyword(s):  
Support Vector Machines ◽  
Gaussian Processes ◽  
Disordered Systems ◽  
Binary Classification ◽  
Computational Cost ◽  
Mean Field ◽  
Strong Support ◽  
Support Vector ◽  
Vector Machines ◽  
Leave One Out

We derive a mean-field algorithm for binary classification with gaussian processes that is based on the TAP approach originally proposed in statistical physics of disordered systems. The theory also yields an approximate leave-one-out estimator for the generalization error, which is computed with no extra computational cost. We show that from the TAP approach, it is possible to derive both a simpler “naive” mean-field theory and support vector machines (SVMs) as limiting cases. For both mean-field algorithms and support vector machines, simulation results for three small benchmark data sets are presented. They show that one may get state-of-the-art performance by using the leave-one-out estimator for model selection and the built-in leave-one-out estimators are extremely precise when compared to the exact leave-one-out estimate. The second result is taken as strong support for the internal consistency of the mean-field approach.

scholarly journals Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

2018 ◽  
Vol 66 (5) ◽  
pp. 1347-1356 ◽  
Author(s):  
Michaela Staňková ◽  
David Hampel
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Tree ◽  
Classification Trees ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Type I ◽  
Vector Machines ◽  
The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

scholarly journals On the parameter optimization of Support Vector Machines for binary classification

2012 ◽  
Vol 9 (3) ◽  
pp. 33-43 ◽  
Author(s):  
Paulo Gaspar ◽  
Jaime Carbonell ◽  
José Luís Oliveira
Keyword(s):  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Performance ◽  
Biological Data ◽  
Parameters Optimization ◽  
Support Vector ◽  
Minimal Risk ◽  
Class Separation ◽  
Vector Machines ◽  
Analyse Data

Summary Classifying biological data is a common task in the biomedical context. Predicting the class of new, unknown information allows researchers to gain insight and make decisions based on the available data. Also, using classification methods often implies choosing the best parameters to obtain optimal class separation, and the number of parameters might be large in biological datasets.Support Vector Machines provide a well-established and powerful classification method to analyse data and find the minimal-risk separation between different classes. Finding that separation strongly depends on the available feature set and the tuning of hyper-parameters. Techniques for feature selection and SVM parameters optimization are known to improve classification accuracy, and its literature is extensive.In this paper we review the strategies that are used to improve the classification performance of SVMs and perform our own experimentation to study the influence of features and hyper-parameters in the optimization process, using several known kernels.

scholarly journals Forecast of Power Grid Investment Scale Based on Support Vector Machine

2020 ◽  
Vol 165 ◽  
pp. 06026
Author(s):  
Yongli Wang ◽  
Yanchao Lu ◽  
Jingyan Wang ◽  
Xiaohui Wang ◽  
Shuo Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Power Grid ◽  
Economic Transformation ◽  
Single Parameter ◽  
Support Vector ◽  
Vector Machines ◽  
New Situation ◽  
Fitting Accuracy

Economic transformation creates a new environment for grid investment. In the situation of high quality development, the traditional investment scale prediction model is no longer applicable. Aiming at the problems of single parameter of grid-driven investment scale prediction model and poor linear fitting accuracy, a provincial medium- and long-term investment scale prediction model based on support vector machine was proposed. Aiming at the single parameter and poor line fitting accuracy of the grid-driven investment scale prediction model under the new situation, the new environment, new directions and new requirements for grid investment are analyzed. Based on the support vector machine algorithm, a medium-and long-term investment scale prediction model for provincial grids based on support vector machines is proposed. The scale of provincial grid investment is expected from 2019 to 2022. The empirical results show that the prediction model constructed in this paper is effective and feasible. It is of great significance to explore the prediction model of medium and long-term investment scale of power grid enterprises in the new situation.

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