Juan Seijas | Universidad Politécnica de Madrid (original) (raw)
Papers by Juan Seijas
Neurocomputing, 2015
ABSTRACT Artificial Metaplasticity learning algorithm is inspired by the biological metaplasticit... more ABSTRACT Artificial Metaplasticity learning algorithm is inspired by the biological metaplasticity property of neurons and Shannon's information theory. It is based on the bio-inspired hypothesis that neurons do not learn in the same amount (metaplasticity of biological learning) from unfrequent patterns than from common ones, as the former are expected to contain more information than the latter (information entropy con-cept). On MLPs, the Artificial Metaplasticity can be formulated as an improvement in regular Backpropagation algorithm by using a variable learning rate affecting all the weights in each iteration step and so re-sembling heterosynaptic plasticity of biological neurons. The variable rate involves statistical inference on the training set and it is common to successfully assume gaussian distribution for the training patterns. Nev-ertheless, gaussian assumption may diverge from the real one and using statistical information extracted from the training patterns may be nec-essary. In this research, robustness to significative variations on gaussian assumption is evaluated using input sets generated with different prob-ability distributions. For the cases where gaussian assumption shows to degrade learning, a general algorithm is applied. This algorithm takes advantage of the inherent statistical inference performed by the MLP through the a posteriori probabilities of input patterns estimation pro-vided by its outputs. The generality of this last algorithm for any input distribution is then demonstrated.
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The problem of known signal detection in Additive White Gaussian Noise is considered. In this pap... more The problem of known signal detection in Additive White Gaussian Noise is considered. In this paper a new detection algorithm based on Discrete Wavelet Transform pre-processing and threshold comparison is introduced. Current approaches described in [7] use the maximum value obtained in the wavelet domain for decision. Here, we use all available information in the wavelet domain with excellent results. Detector performance is presented in Probability of detection curves for a fixed probability of false alarm.
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Current approaches in pulse detection use domain transformations so as to concentrate frequency r... more Current approaches in pulse detection use domain transformations so as to concentrate frequency related information that can be distinguishable from noise. In real cases we do not know when the pulse will begin, so we need a time search process in which time windows are scheduled and analysed. Each window can contain the pulsed signal (either complete or incomplete) and / or noise. In this paper a simple search process will be introduced, allowing the algorithm to process more information, upgrading the capabilities in terms of probability of detection (Pd) and probability of false alarm (Pfa).
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ArXiv, 2005
The problem of known signal detection in Additive White Gaussian Noise is considered. In previous... more The problem of known signal detection in Additive White Gaussian Noise is considered. In previous work, a new detection scheme was introduced by the authors, and it was demonstrated that optimum performance cannot be reached in a real implementation. In this paper we analyse Support Vector Machines (SVM) as an alternative, evaluating the results in terms of Probability of detection curves for a fixed Probability of false alarm.
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2001 Sacramento, CA July 29-August 1,2001, 1998
Multiple alignment of nucleic acid, or amino acid, sequences is one of the most commonly used tec... more Multiple alignment of nucleic acid, or amino acid, sequences is one of the most commonly used techniques in sequence analysis. These techniques are usually characterized by great computational complexity. We present a stochastic approach based on Genetic Algorithms (GA). GA are strategies of random searching that optimize an objective function which is a measure of alignment quality (distance). We randomly create a population of alignments among the sequences. Each individual in the population represents (in an efficient way) some underlying operations on the sequences and they evolve, by means of natural selection, to better populations where most of the individuals represent suboptimal operations for alignment of those sequences. A set of well-known test cases is used as a reference to evaluate the efficiency of the optimization process. The improvement of the effectiveness is one of the first conclusions when a comparison, among this algorithm versus the classical ones, is performed. At the same time, our GA presents some characteristics as robustness, convergence to solution, extraordinary capability of generalization and a easiness of being coded for parallel processing architectures, that make our GA very suitable for multiple molecular biology sequences analysis.
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Iscas, 1995
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Lecture Notes in Computer Science, 2002
We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything ... more We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything (except elitism operator) is the same in both applications, including the values of GA parameters. Whereas the two applications are very different: One of them presents a deterministic behavior during the GA iterations, and the other one presents a stochastic behavior. For this different nature of the
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Iwann, 2009
Abstract. Breast cancer is one of the leading causes to women mortality in the world. Clusters of... more Abstract. Breast cancer is one of the leading causes to women mortality in the world. Clusters of Microcalcifications (MCCs) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. Coordinate Logic Filters (CLF), are very efficient in digital signal processing applications, such as noise removal, magnification, opening, closing, skeletonization, and coding, as well as in edge detection, feature extraction, and fractal modelling. This paper presents an edge detector ...
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ABSTRACT
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WSEAS Transactions on Communications
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Support vector machines is the most recent algorithm in the machine learning community. After a b... more Support vector machines is the most recent algorithm in the machine learning community. After a bit less than a decade of live, it has displayed many advantages with respect to the best old methods: generalization capacity, ease of use, solution uniqueness. It has also shown some disadvantages: maximum data handling and speed in the training phase. However, these disadvantages will be overcome in the near future, as computer power increases, leaving an all-purpose learning method both cheap to use and giving the best performance. This chapter provides an overview about the main SVM configuration, its mathematical applications and the easiest implementation.
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Different edge-detection algorithms developed for high-speckle SAR images are evaluated and compa... more Different edge-detection algorithms developed for high-speckle SAR images are evaluated and compared. Two evaluation criterions have been selected: (i) quality of the detected edges, specially considering false alarms, edge thickness and level of discontinuities on detected edges. (ii) computational efficiency. Some modifications on the best-performance algorithms regarding edge quality are proposed, at the expense of a certain computational load increase.
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Lecture Notes in Computer Science, 2002
We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything ... more We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything (except elitism operator) is the same in both applications, including the values of GA parameters. Whereas the two applications are very different: One of them presents a deterministic behavior during the GA iterations, and the other one presents a stochastic behavior. For this different nature of the
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Proceedings of ISCAS'95 - International Symposium on Circuits and Systems, 1995
This paper presents a procedure for optimising a neural network architecture used in a system for... more This paper presents a procedure for optimising a neural network architecture used in a system for spacecraft attitude and position determination. The procedure establishes the neural network structure and the training algorithm. A new version of Basic-Evolutive algorithm is presented, Basic-Evolutive 1 and Basic-Evolutive 2 algorithms are capable of setting the appropriate dimension of the neural network and the adequate
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Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994
A straightforward method for implementing a neural network (NN) solution in the field of image pr... more A straightforward method for implementing a neural network (NN) solution in the field of image processing, for attitude and position determination, is presented in this paper. The proposed evolutive training algorithm is capable of setting the appropriate dimension of the neural network and the adequate weights interconnecting the neurons. The recommended solution is based on simulation results
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Applications and Science of Computational Intelligence, 1998
ABSTRACT
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Neurocomputing, 2015
ABSTRACT Artificial Metaplasticity learning algorithm is inspired by the biological metaplasticit... more ABSTRACT Artificial Metaplasticity learning algorithm is inspired by the biological metaplasticity property of neurons and Shannon's information theory. It is based on the bio-inspired hypothesis that neurons do not learn in the same amount (metaplasticity of biological learning) from unfrequent patterns than from common ones, as the former are expected to contain more information than the latter (information entropy con-cept). On MLPs, the Artificial Metaplasticity can be formulated as an improvement in regular Backpropagation algorithm by using a variable learning rate affecting all the weights in each iteration step and so re-sembling heterosynaptic plasticity of biological neurons. The variable rate involves statistical inference on the training set and it is common to successfully assume gaussian distribution for the training patterns. Nev-ertheless, gaussian assumption may diverge from the real one and using statistical information extracted from the training patterns may be nec-essary. In this research, robustness to significative variations on gaussian assumption is evaluated using input sets generated with different prob-ability distributions. For the cases where gaussian assumption shows to degrade learning, a general algorithm is applied. This algorithm takes advantage of the inherent statistical inference performed by the MLP through the a posteriori probabilities of input patterns estimation pro-vided by its outputs. The generality of this last algorithm for any input distribution is then demonstrated.
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The problem of known signal detection in Additive White Gaussian Noise is considered. In this pap... more The problem of known signal detection in Additive White Gaussian Noise is considered. In this paper a new detection algorithm based on Discrete Wavelet Transform pre-processing and threshold comparison is introduced. Current approaches described in [7] use the maximum value obtained in the wavelet domain for decision. Here, we use all available information in the wavelet domain with excellent results. Detector performance is presented in Probability of detection curves for a fixed probability of false alarm.
Bookmarks Related papers MentionsView impact
Current approaches in pulse detection use domain transformations so as to concentrate frequency r... more Current approaches in pulse detection use domain transformations so as to concentrate frequency related information that can be distinguishable from noise. In real cases we do not know when the pulse will begin, so we need a time search process in which time windows are scheduled and analysed. Each window can contain the pulsed signal (either complete or incomplete) and / or noise. In this paper a simple search process will be introduced, allowing the algorithm to process more information, upgrading the capabilities in terms of probability of detection (Pd) and probability of false alarm (Pfa).
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ArXiv, 2005
The problem of known signal detection in Additive White Gaussian Noise is considered. In previous... more The problem of known signal detection in Additive White Gaussian Noise is considered. In previous work, a new detection scheme was introduced by the authors, and it was demonstrated that optimum performance cannot be reached in a real implementation. In this paper we analyse Support Vector Machines (SVM) as an alternative, evaluating the results in terms of Probability of detection curves for a fixed Probability of false alarm.
Bookmarks Related papers MentionsView impact
2001 Sacramento, CA July 29-August 1,2001, 1998
Multiple alignment of nucleic acid, or amino acid, sequences is one of the most commonly used tec... more Multiple alignment of nucleic acid, or amino acid, sequences is one of the most commonly used techniques in sequence analysis. These techniques are usually characterized by great computational complexity. We present a stochastic approach based on Genetic Algorithms (GA). GA are strategies of random searching that optimize an objective function which is a measure of alignment quality (distance). We randomly create a population of alignments among the sequences. Each individual in the population represents (in an efficient way) some underlying operations on the sequences and they evolve, by means of natural selection, to better populations where most of the individuals represent suboptimal operations for alignment of those sequences. A set of well-known test cases is used as a reference to evaluate the efficiency of the optimization process. The improvement of the effectiveness is one of the first conclusions when a comparison, among this algorithm versus the classical ones, is performed. At the same time, our GA presents some characteristics as robustness, convergence to solution, extraordinary capability of generalization and a easiness of being coded for parallel processing architectures, that make our GA very suitable for multiple molecular biology sequences analysis.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Iscas, 1995
Bookmarks Related papers MentionsView impact
Lecture Notes in Computer Science, 2002
We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything ... more We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything (except elitism operator) is the same in both applications, including the values of GA parameters. Whereas the two applications are very different: One of them presents a deterministic behavior during the GA iterations, and the other one presents a stochastic behavior. For this different nature of the
Bookmarks Related papers MentionsView impact
Iwann, 2009
Abstract. Breast cancer is one of the leading causes to women mortality in the world. Clusters of... more Abstract. Breast cancer is one of the leading causes to women mortality in the world. Clusters of Microcalcifications (MCCs) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. Coordinate Logic Filters (CLF), are very efficient in digital signal processing applications, such as noise removal, magnification, opening, closing, skeletonization, and coding, as well as in edge detection, feature extraction, and fractal modelling. This paper presents an edge detector ...
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
ABSTRACT
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
WSEAS Transactions on Communications
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Support vector machines is the most recent algorithm in the machine learning community. After a b... more Support vector machines is the most recent algorithm in the machine learning community. After a bit less than a decade of live, it has displayed many advantages with respect to the best old methods: generalization capacity, ease of use, solution uniqueness. It has also shown some disadvantages: maximum data handling and speed in the training phase. However, these disadvantages will be overcome in the near future, as computer power increases, leaving an all-purpose learning method both cheap to use and giving the best performance. This chapter provides an overview about the main SVM configuration, its mathematical applications and the easiest implementation.
Bookmarks Related papers MentionsView impact
Different edge-detection algorithms developed for high-speckle SAR images are evaluated and compa... more Different edge-detection algorithms developed for high-speckle SAR images are evaluated and compared. Two evaluation criterions have been selected: (i) quality of the detected edges, specially considering false alarms, edge thickness and level of discontinuities on detected edges. (ii) computational efficiency. Some modifications on the best-performance algorithms regarding edge quality are proposed, at the expense of a certain computational load increase.
Bookmarks Related papers MentionsView impact
Lecture Notes in Computer Science, 2002
We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything ... more We present a Genetic Algorithm (GA) capable of optimizing two different applications. Everything (except elitism operator) is the same in both applications, including the values of GA parameters. Whereas the two applications are very different: One of them presents a deterministic behavior during the GA iterations, and the other one presents a stochastic behavior. For this different nature of the
Bookmarks Related papers MentionsView impact
Proceedings of ISCAS'95 - International Symposium on Circuits and Systems, 1995
This paper presents a procedure for optimising a neural network architecture used in a system for... more This paper presents a procedure for optimising a neural network architecture used in a system for spacecraft attitude and position determination. The procedure establishes the neural network structure and the training algorithm. A new version of Basic-Evolutive algorithm is presented, Basic-Evolutive 1 and Basic-Evolutive 2 algorithms are capable of setting the appropriate dimension of the neural network and the adequate
Bookmarks Related papers MentionsView impact
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994
A straightforward method for implementing a neural network (NN) solution in the field of image pr... more A straightforward method for implementing a neural network (NN) solution in the field of image processing, for attitude and position determination, is presented in this paper. The proposed evolutive training algorithm is capable of setting the appropriate dimension of the neural network and the adequate weights interconnecting the neurons. The recommended solution is based on simulation results
Bookmarks Related papers MentionsView impact
Applications and Science of Computational Intelligence, 1998
ABSTRACT
Bookmarks Related papers MentionsView impact