Catálogo de publicaciones - libros

Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to nonnegative Blind Source Separation (BSS) problems. In this paper we present first results of an extension to the NMF algorithm which solves the BSS problem when the underlying sources are sufficiently sparse. As the proposed target function is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Application to a microarray data set will be considered also.

With the rapid increase in connectivity and accessibility of computer systems over the internet which has resulted in frequent opportunities for intrusions and attacks, intrusion detection on the network has become a crucial issue for computer system security. Methods based on hand-coded rule sets are laborous to build and not very reliable. This problem has led to an increasing interest in intrusion detection techniques based upon machine learning or data mining. However, traditional data mining based intrusion detection systems use single classifier in their detection engines. In this paper, we propose a meta learning based method for intrusion detection by MultiBoosting multi classifiers. MultiBoosting can form decision committees by combining AdaBoost with wagging. It is able to harness both AdaBoost’s high bias and variance reduction with wagging’s superior variance reduction. Experiments results show that MultiBoosting can improve the detection performance of state-of-art machine learning based intrusion detection techniques. Furthermore, we present a Symmetrical Uncertainty (SU) based method for reducing network connection features to make MultiBoosting more efficient in real-time network environment, in the meanwhile, keep the detection performance unundermined and in some cases, even further improved.

Meta-Learning has been used to select algorithms based on the features of the problems being tackled. Each training example in this context, i.e. each meta-example, stores the features of a given problem and the performance information obtained by the candidate algorithms in the problem. The construction of a set of meta-examples may be costly, since the algorithms performance is usually defined through an empirical evaluation on the problem at hand. In this context, we proposed the use of Active Learning to select only the relevant problems for meta-example generation. Hence, the need for empirical evaluations of the candidate algorithms is reduced. Experiments were performed using the classification uncertainty of the k-NN algorithm as the criteria for active selection of problems. A significant gain in performance was yielded by using the Active Learning method.

We investigate the properties of coupled co-learning systems during the emergence of communication. Co-learning systems are more complex than individual learning systems because of being dependent on the learning process of each other, thus risking divergence. We developed a neural network approach and implemented a concept that we call reconstruction principle, which we found adequate for overcoming the instability problem. Experimental simulations were performed to test the emergence of both compositional and holistic communication. The results show that compositional communication is favorable when learning performance is considered, however it is more error-prone to differences in the conceptual representations of the individual systems. We show that our architecture enables the adjustment of the differences in the individual representations in case of compositional communication.

Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. One of the important factors to characterize behavior of a complex-valued neural network is its activation function which is a nonlinear complex function. In complex-valued neural networks, there are several possibilities in choosing an activation function because of a wide variety of complex functions. This paper proposes three models of orthogonal type dynamic associative memories using complex-valued neural networks with three different activation functions. We investigate their behavior as associative memories theoretically. Comparisons are also made among these three models in terms of dynamics and storage capabilities.

We analyze a discrete-time quaternionic Hopfield neural network with continuous state variables updated asynchronously. The state of a neuron takes quaternionic value which is four-dimensional hypercomplex number. Two types of the activation function for updating neuron states are introduced and examined. The stable states of the networks are demonstrated through an example of small network.

Neural learning algorithms based on optimization on manifolds differ by the way the single learning steps are effected on the neural system’s parameter space. In this paper, we present a class counting four neural learning algorithms based on the differential geometric concept of mappings from the tangent space of a manifold to the manifold itself. A learning stepsize adaptation theory is proposed as well under the hypothesis of additiveness of the learning criterion. The numerical performances of the discussed algorithms are illustrated on a learning task and are compared to a reference algorithm known from literature.

Neural computation in Clifford algebras, which include familiar complex numbers and quaternions as special cases, has recently become an active research field. As always, neurons are the atoms of computation. The paper provides a general notion for the Hessian matrix of Clifford neurons of an arbitrary algebra. This new result on the dynamics of Clifford neurons then allows the computation of optimal learning rates. A thorough discussion of error surfaces together with simulation results for different neurons is also provided. The presented contents should give rise to very efficient second–order training methods for Clifford Multi-layer perceptrons in the future.

A multilayer neural network based on multi-valued neurons (MLMVN) is a new powerful tool for solving classification, recognition and prediction problems. This network has a number of specific properties and advantages that follow from the nature of a multi-valued neuron (complex-valued weights and inputs/outputs lying on the unit circle). Its backpropagation learning algorithm is derivative-free. The learning process converges very quickly, and the learning rate for all neurons is self-adaptive. The functionality of the MLMVN is higher than the one of the traditional feedforward neural networks and a variety of kernel-based networks. Its higher flexibility and faster adaptation to the mapping implemented make it possible to solve complex classification problems using a simpler network. In this paper, we show that the MLMVN can be successfully used for solving two selected classification problems in bioinformatics.

Computer Generated Holograms (CGHs) are commonly used in optical tweezers which are employed in various research fields. Frame interpolation using coherent neural networks (CNNs) based on correlation learning can be used to generate holographic movies efficiently. However, the error that appears in the interpolated CGH images need to be reduced even further so that the method with frame interpolation can be accepted for use generally. In this paper, we propose a new hybrid CNN learning method that is able to generate the movies almost just as efficiently and yet reduces even more error that is present in the generated holographic images as compared to the method based solely on correlation learning.