Departamento de Ciencias de la Computación

Permanent URI for this community

https://hdl.handle.net/20.500.12590/17959

Browse

Browsing Departamento de Ciencias de la Computación by Subject "Algorithms"

Now showing 1 - 6 of 6
  • No Thumbnail Available
    Item
    A new boosting design of Support Vector Machine classifiers
    (Elsevier, 2015) Mayhua López, Efraín; Gómez Verdejo, Vanessa; Figueiras Vidal, Aníbal
    Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly classified. If common kernel Support Vector Machines (SVMs) are used as basic learners to construct a Real AdaBoost ensemble, the resulting ensemble can be easily compacted into a monolithic architecture by simply combining the weights that correspond to the same kernels when they appear in different learners, avoiding to increase the operation computational effort for the above potential advantage. This way, the performance advantage that boosting provides can be obtained for monolithic SVMs, i.e., without paying in classification computational effort because many learners are needed. However, SVMs are both stable and strong, and their use for boosting requires to unstabilize and to weaken them. Yet previous attempts in this direction show a moderate success. In this paper, we propose a combination of a new and appropriately designed subsampling process and an SVM algorithm which permits sparsity control to solve the difficulties in boosting SVMs for obtaining improved performance designs. Experimental results support the effectiveness of the approach, not only in performance, but also in compactness of the resulting classifiers, as well as that combining both design ideas is needed to arrive to these advantageous designs. © 2014 Elsevier B.V.All rights reserved.
  • No Thumbnail Available
    Item
    An approach to real-coded quantum inspired evolutionary algorithm using particles filter
    (Institute of Electrical and Electronics Engineers Inc., 2016) Chire Saire, Josimar Edinson; Túpac Valdivia, Yván Jesús
    This work proposes, implements and evaluates the FP-QIEA-R model as a new quantum inspired evolutionary algorithm based on the concept of quantum superposition that allows the optimization process to be carried on with a smaller number of evaluations. This model is based on a QIEA-R, but instead of just using quantum individuals based on uniform probability density functions, where the update consists on change the width and mean of each pdf; this proposal uses a combined mechanism inspired in particle filter and multilinear regression, re-sampling and relative frequency with the QIEA-R to estimate the probability density functions in a better way. To evaluate this proposal, some experiments under benchmark functions are presented. The obtained statistics from the outcomes show the improved performance of this proposal optimizing numerical problems. © 2015 IEEE.
  • No Thumbnail Available
    Item
    DBM*-Tree: An efficient metric acces method
    (Scopus, 2007) Ocsa, Alexander; Cuadros Vargas, Ernesto
    In this paper we propose a new dynamic Metric Access Method (MAM) called DBM*-Tree, which uses precomputed distances to reduce the construction cost avoiding repeated calculus of distance. Making use of the pre-calculated distances cost of similarity queries are also reduced by taking various local representative objects in order to increment the pruning of irrelevant elements during the query. We also propose a new algorithm to select the suitable subtree in the insertion operation, which is an evolution of the previous methods. Empiric tests on real and synthetic data have shown evidence that DBM*-Tree requires 25 % less average distance computing than Density Based Metric Tree (DBM-Tree) which is one of the most efficient and recent MAM found in the literature. © Copyright 2007 ACM.
  • No Thumbnail Available
    Item
    GPS assistance taximeter suited to the characteristics of a city
    (CEUR-WS, 2015) Santisteban Pablo, Julio Omar; Aranzaens Cam, Ximena Lucia
    Today almost every one use a taxi, in many countries the rate is agreed upon the service is taken, in many cases the rate is too low or high and the service is not taken, on the other hand taximeters provides good estimated of the service but are too complex to install in a cab and maintain. Today we can get advance of GPS technology to improve and enhance the taximeters while the rate is calculate base on the different features of a city. We propose a novel algorithm to calculate rate using GPS data and base on the different features of a city; we build a prototype and test it with very good results. Copyright © 2015 for the individual papers by the papers' authors.
  • No Thumbnail Available
    Item
    Introduction to the SAM-S M* and MAM-S M* families
    (Scopus, 2005) Cuadros Vargas, Ernesto; Romero, Francelin
    In this paper, two new families of constructive Self-Organizing Maps (SOMs), SAM-SOM* and MAM-SOM*, are proposed. These families are specially useful for information retrieval from large databases, high-dimensional spaces and complex distance functions which usually consume a long time. They are generated by incorporating Spatial Access Method (SAM) and Metric Access Method (MAM) into SOM with the maximum insertion rate, i.e. the case when a new unit is created for each pattern presented to the network. In this specific case, the network presents interesting advantages and acquires new properties which are quite different of traditional SOM. In a constructive SOM, if new units are rarely inserted into network, the training algorithm would probably need a long time to converge. On the other hand, if new units are inserted frequently, the training algorithm would not have enough time to adapt these units to the data distribution. Besides, training time is increased because the search for the winning neuron is traditionally performed sequentially. The use of SAM and MAM combined with SOM open the possibility of training constructive SOM with as much units as existing patterns with less time and interesting advantages compared with both models: Kohonen network SOM and SAM-SOM model (SOM using SAM). Advantages and drawbacks of these new families are also discussed. These new families are useful to improve both SOM and SAM techniques.
  • No Thumbnail Available
    Item
    Optimal selection of factors using Genetic Algorithms and Neural Networks for the prediction of students' academic
    (Institute of Electrical and Electronics Engineers Inc., 2016) Echegaray Calderon, Omar; Barrios Aranibar, Dennis
    In this research, we propose to use a Genetic Algorithm with an Artificial Neural Network as fitness function in order to solve one of the most important problems in predicting academic success in higher education environments. Which is to find what are the factors that affect the students' academic performance. Also, using the same Artificial Neural Network as a predictor. To solve the problem, each individual of the genetic algorithm represents a group of factors, which will be evaluated with the fitness function seeking to obtain the optimal individual (group of factors) to predict academic performance. Then, with the same Artificial Neural Network we will classify students' academic grades in order to predict their semester final grades. With this technique, it was possible to reduce the initial amount of 39 factors (founded in the literature) to only 8. The prediction accuracy is 84.86%. © 2015 IEEE.