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Now showing items 41-50 of 93
Application of machine learning techniques to weather forecasting
(2019-02-11)
El pronóstico del tiempo es, incluso hoy en día, una actividad realizada principalmente por humanos. Si bien las simulaciones por computadora desempeñan un papel importante en el modelado del estado y la evolución de la ...
Approaching Sentiment Analysis by Using Semi-supervised Learning of Multidimensional Classifiers
(2011)
Sentiment Analysis is defined as the computational study of opinions, sentiments and emotions
expressed in text. Within this broad field, most of the work has been focused on either Sentiment
Polarity classification, ...
Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance
(2014-01-22)
[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and ...
Sampling and learning the Mallows and Weighted Mallows models under the Hamming distance
(2014-01-22)
[EN]In this paper we deal with distributions over permutation spaces. The Mallows model is the mode l in use. The associated distance for permutations is the Hamming distance.
Sampling and learning the Mallows model under the Ulam distance
(2014-01-22)
[EN]In this paper we deal with probability distributions over permutation spaces. The Probability model in use is the Mallows model. The distance for permutations that the model uses in the Ulam distance.
Data analysis advances in marine science for fisheries management : supervised classification application
(Servicio Editorial de la Universidad del País Vasco/Euskal Herriko Unibertsitatearen Argitalpen Zerbitzua, 2011-05-06)
La gestión de pesquerías tiene un gran impacto a muchos niveles: biológico, económico, social y político con gran incertidumbre sobre las relaciones entre el clima, los peces y las decisiones de gestión. Esta tesis doctoral, ...
Learning Probability Distributions over Permutations by Means of Fourier Coefficients
(2011)
A large and increasing number of data mining domains consider data
that can be represented as permutations. Therefore, it is important to
devise new methods to learn predictive models over datasets of permutations.
However, ...
Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification
(2015-10-23)
Las técnicas de análisis de datos permitenextraer información de un conjunto de datos. Hoy en día, con la explosión delas nuevas tecnologías, el enorme volumen de datos que una amplia variedadde dispositivos recogen y ...
Efficient implementation of symplectic implicit Runge-Kutta schemes with simplified Newton iterations
(2017-03-30)
We are concerned with the efficient implementation of symplectic
implicit Runge-Kutta (IRK) methods applied to systems of (non-necessarily
Hamiltonian) ordinary differential equations by means of Newton-like iterations. ...
Reducing and monitoring round-off error propagation for symplectic implicit Runge-Kutta schemes
(2017-01-31)
We propose an implementation of symplectic implicit Runge-Kutta schemes for highly accurate numerical integration of non-stiff Hamiltonian systems based on fixed point iteration. Provided that the computations are done in ...