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A factor graph based genetic algorithm

B. Hoda Helmi, Adel T. Rahmani, Martin Pelikan (2014)

International Journal of Applied Mathematics and Computer Science

We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems...

A Generalized Model of PAC Learning and its Applicability

Thomas Brodag, Steffen Herbold, Stephan Waack (2014)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

We combine a new data model, where the random classification is subjected to rather weak restrictions which in turn are based on the Mammen−Tsybakov [E. Mammen and A.B. Tsybakov, Ann. Statis. 27 (1999) 1808–1829; A.B. Tsybakov, Ann. Statis. 32 (2004) 135–166.] small margin conditions, and the statistical query (SQ) model due to Kearns [M.J. Kearns, J. ACM 45 (1998) 983–1006] to what we refer to as PAC + SQ model. We generalize the class conditional constant noise (CCCN) model introduced by Decatur...

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

Sotirios K. Goudos (2016)

Open Mathematics

A major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization...

Bias-variance decomposition in Genetic Programming

Taras Kowaliw, René Doursat (2016)

Open Mathematics

We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the overall error and its decomposed contributions. These parameters are the maximum program size, the initial population, and the function set used. We confirm and quantify several insights into the practical usage of GP, most notably that (a) the...

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

Hideaki Itoh, Hisao Fukumoto, Hiroshi Wakuya, Tatsuya Furukawa (2015)

International Journal of Applied Mathematics and Computer Science

The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden...

Density estimation with quadratic loss: a confidence intervals method

Pierre Alquier (2008)

ESAIM: Probability and Statistics

We propose a feature selection method for density estimation with quadratic loss. This method relies on the study of unidimensional approximation models and on the definition of confidence regions for the density thanks to these models. It is quite general and includes cases of interest like detection of relevant wavelets coefficients or selection of support vectors in SVM. In the general case, we prove that every selected feature actually improves the performance of the estimator. In the case...

Graphics card as a cheap supercomputer

Přikryl, Jan (2013)

Programs and Algorithms of Numerical Mathematics

The current powerful graphics cards, providing stunning real-time visual effects for computer-based entertainment, have to accommodate powerful hardware components that are able to deliver the photo-realistic simulation to the end-user. Given the vast computing power of the graphics hardware, its producers very often offer a programming interface that makes it possible to use the computational resources of the graphics processors (GPU) to more general purposes. This step gave birth to the so-called...

Learning discrete categorial grammars from structures

Jérôme Besombes, Jean-Yves Marion (2008)

RAIRO - Theoretical Informatics and Applications

We define the class of discrete classical categorial grammars, similar in the spirit to the notion of reversible class of languages introduced by Angluin and Sakakibara. We show that the class of discrete classical categorial grammars is identifiable from positive structured examples. For this, we provide an original algorithm, which runs in quadratic time in the size of the examples. This work extends the previous results of Kanazawa. Indeed, in our work, several types can be associated to a word...

Learning tree languages from text

Henning Fernau (2007)

RAIRO - Theoretical Informatics and Applications

We study the problem of learning regular tree languages from text. We show that the framework of function distinguishability, as introduced by the author in [Theoret. Comput. Sci.290 (2003) 1679–1711], can be generalized from the case of string languages towards tree languages. This provides a large source of identifiable classes of regular tree languages. Each of these classes can be characterized in various ways. Moreover, we present a generic inference algorithm with polynomial update time...

On the Optimality of Sample-Based Estimates of the Expectation of the Empirical Minimizer***

Peter L. Bartlett, Shahar Mendelson, Petra Philips (2010)

ESAIM: Probability and Statistics

We study sample-based estimates of the expectation of the function produced by the empirical minimization algorithm. We investigate the extent to which one can estimate the rate of convergence of the empirical minimizer in a data dependent manner. We establish three main results. First, we provide an algorithm that upper bounds the expectation of the empirical minimizer in a completely data-dependent manner. This bound is based on a structural result due to Bartlett and Mendelson, which relates...

PAC Learning under Helpful Distributions

François Denis, Rémi Gilleron (2010)

RAIRO - Theoretical Informatics and Applications

A PAC teaching model -under helpful distributions -is proposed which introduces the classical ideas of teaching models within the PAC setting: a polynomial-sized teaching set is associated with each target concept; the criterion of success is PAC identification; an additional parameter, namely the inverse of the minimum probability assigned to any example in the teaching set, is associated with each distribution; the learning algorithm running time takes this new parameter into account. ...

PAC learning under helpful distributions

François Denis, Rémi Gilleron (2001)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

A PAC teaching model – under helpful distributions – is proposed which introduces the classical ideas of teaching models within the PAC setting: a polynomial-sized teaching set is associated with each target concept; the criterion of success is PAC identification; an additional parameter, namely the inverse of the minimum probability assigned to any example in the teaching set, is associated with each distribution; the learning algorithm running time takes this new parameter into account. An Occam...

Prediction of time series by statistical learning: general losses and fast rates

Pierre Alquier, Xiaoyin Li, Olivier Wintenberger (2013)

Dependence Modeling

We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates for the...

Theory of classification : a survey of some recent advances

Stéphane Boucheron, Olivier Bousquet, Gábor Lugosi (2005)

ESAIM: Probability and Statistics

The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have led to these recent results.

Theory of Classification: a Survey of Some Recent Advances

Stéphane Boucheron, Olivier Bousquet, Gábor Lugosi (2010)

ESAIM: Probability and Statistics

The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have led to these recent results.

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