Paul Bosch

Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile

In this work we apply multi-class support vector machines (SVMs) and a multi-class stochastic SVM formulation to the classification of fish schools of three species: anchovy, common sardine, and Jack Mackerel, and we compare their performance. The data used come from acoustic measurements in southern-central Chile. These classifications were carried out by using a diver set of descriptors including morphology, bathymetry, energy, and space positions. In both type of formulations, the deterministic and the stochastic one, the strategy used to classify multi-class SVM consists in employing the criterion one-species-against-the-Rest. We thus provide an empirical way to adjust the parameters involved in the stochastic classifiers with the aim of improving its performance. When this procedure is applied to the classification of fish schools we obtain a classifier with a better performance than the deterministic classifier.

Duality for inexact semi-infinite linear programming

The aim of this work is to generalize strong duality theorems for inexact linear programming and to derive duality results for inexact semi-infinite programming problems. We give a detailed proof of the general result, using the Dubovitskii–Milyutin approach. The last section contains applications to inexact problems and a few comments for further developments.

Two-Stage Stochastic Programs with Mixed Probabilities

We extend the traditional two-stage linear stochastic program by probabilistic constraints imposed in the second stage. This adds nonlinearity such that basic arguments for analyzing the structure of linear two-stage stochastic programs have to be rethought from the very beginning. We identify assumptions under which the problem is structurally sound and behaves stably under perturbations of probability measures.

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.

Clasificación acústica de anchoveta (Engraulis ringens) y sardina común (Strangomera bentincki) mediante máquinas de vectores soporte en la zona centro-sur de Chile: efecto de la calibración de los parámetros en la matriz de confusión

RESUMEN. Se clasifico la anchoveta (Engraulis ringens) y sardina comun (Strangomera bentincki) detectadas mediante equipos acusticos en la zona centro-sur de Chile, mediante el metodo de Maquinas de Vectores Soporte (SVM). Para esto se utilizaron descriptores de cardumenes extraidos desde ecogramas, que fueron clasificados como morfologicos, batimetricos, energeticos y posicional espacial. Para lograr clasificaciones precisas mediante la utilizacion de esta metodologia, fue necesario optimizar parametros correspondientes al Kernel-Gaussiano, γ y de penalizacion del modelo C, mediante el analisis del efecto de la calibracion sobre las matrices de confusion resultantes de la clasificacion de las especies analizadas. El metodo SVM ajusto correctamente el 95,3% de los cardumenes de anchoveta y sardina comun. Los parametros optimos del Kernel-Gaussiano γ y de penalizacion C obtenidos mediante la metodologia propuesta fueron γ = 450 y C = 0,95, respectivamente. Los parametros mencionados incidieron de manera importante en la matriz de confusion y los porcentajes de clasificacion final, por lo que se sugiere establecer, en aplicaciones futuras de este metodo, un protocolo experimental de calibracion. La sardina comun fue la especie con menor error de clasificacion en el conjunto de las matrices de confusion. El descriptor correspondiente a profundidad del fondo fue el mas sensible al SVM, la segunda variable en importancia es el descriptor distancia a la costa. Palabras clave: maquinas de vectores soporte, clasificacion de especies, hidroacustica, peces pelagicos, anchoveta, sardina, Chile. Acoustic classification of anchovy (Engraulis ringens) and sardine (Strangomera bentincki) using support vector machines in central-southern Chile: effect of parameter calibration on the confusion matrix

Application of classifiers: Support vector machines, artificial neural networks and classification trees to identify acoustic schools

The purpose of this study was to compare the results of the classification of the pelagic fish species, the common sardine, anchovy, and jack mackerel with classification trees (CART), Support Vector Machine (SVM) and artificial neural network (multilayer perceptron, MLP), using mono-frequency acoustic data in southern-central Chile. The classifiers had similar performances, those of the MLP and SVM being the same, while t hat of CART was the lowest. The separation of anchovy and common sardine is considered acceptable with all methods, 90.8% for anchovy and between 87.4% (CART) and 90.3% (MLP) for sardine. These performances were higher than that for the jack mackerel, 77.8% (CART), 81.5% (MLP) and 85.2% (SVM). There is concordance on the groups of descriptors (bathymetric and positional) considered as effective for classification in all methods, but the importance of the descriptors presented by each method is not fully concordant. The energetic and morphological descriptor had low incidence. We recommend trying many classifiers to identify acoustic schools as a good practice.

A Numerical Method for Two-Stage Stochastic Programs under Uncertainty

Motivated by problems coming from planning and operational management in power generation companies, this work extends the traditional two-stage linear stochastic program by adding probabilistic constraints in the second stage. In this work we describe, under special assumptions, how the two-stage stochastic programs with mixed probabilities can be treated computationally. We obtain a convex conservative approximations of the chance constraints defined in second stage of our model and use Monte Carlo simulation techniques for approximating the expectation function in the first stage by the average. This approach raises with another question: how to solve the linear program with the convex conservative approximation (nonlinear constrains) for each scenario?

Extension theorem for complex Clifford algebras-valued functions on fractal domains.

Monogenic extension theorem of complex Clifford algebras-valued functions over a bounded domain with fractal boundary is obtained. The paper is dealing with the class of Hölder continuous functions. Applications to holomorphic functions theory of several complex variables as well as to that of the so-called biregular functions will be deduced directly from the isotonic approach.

Necessary conditions and duality for inexact nonlinear semi-infinite programming problems

First order necessary conditions and duality results for general inexact nonlinear programming problems formulated in nonreflexive spaces are obtained. The Dubovitskii–Milyutin approach is the main tool used. Particular cases of linear and convex programs are also analyzed and some comments about a comparison of the obtained results with those existing in the literature are given.