Manuel Grana Romay – författare

In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discussions of “learning without iterative tuning".This book covers algorithms and applications of ELM. It gives readers a glance of the newest developments of ELM.

In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discussions of “learning without iterative tuning".This book covers algorithms and applications of ELM. It gives readers a glance of the newest developments of ELM.

th The 5 International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) has become a unique, established and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques, and bringing the most relevant achievements in this field. Overcoming the rigid encasing imposed by the arising orthodoxy in the field of arti- cial intelligence, which has led to the partition of researchers into so-called areas or fields, interest in hybrid intelligent systems is growing because they give freedom to design innovative solutions to the ever-increasing complexities of real-world pr- lems. Noise and uncertainty call for probabilistic (often Bayesian) methods, while the huge amount of data in some cases asks for fast heuristic (in the sense of suboptimal and ad-hoc) algorithms able to give answers in acceptable time frames. High dim- sionality demands linear and non-linear dimensionality reduction and feature extr- tion algorithms, while the imprecision and vagueness call for fuzzy reasoning and linguistic variable formalization. Nothing impedes real-life problems to mix diffic- ties, presenting huge quantities of noisy, vague and high-dimensional data; therefore, the design of solutions must be able to resort to any tool of the trade to attack the problem. Combining diverse paradigms poses challenging problems of computational and methodological interfacing of several previously incompatible approaches. This is, thus, the setting of HAIS conference series, and its increasing success is the proof of the vitality of this exciting field.

th The 5 International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) has become a unique, established and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques, and bringing the most relevant achievements in this field. Overcoming the rigid encasing imposed by the arising orthodoxy in the field of arti- cial intelligence, which has led to the partition of researchers into so-called areas or fields, interest in hybrid intelligent systems is growing because they give freedom to design innovative solutions to the ever-increasing complexities of real-world pr- lems. Noise and uncertainty call for probabilistic (often Bayesian) methods, while the huge amount of data in some cases asks for fast heuristic (in the sense of suboptimal and ad-hoc) algorithms able to give answers in acceptable time frames. High dim- sionality demands linear and non-linear dimensionality reduction and feature extr- tion algorithms, while the imprecision and vagueness call for fuzzy reasoning and linguistic variable formalization. Nothing impedes real-life problems to mix diffic- ties, presenting huge quantities of noisy, vague and high-dimensional data; therefore, the design of solutions must be able to resort to any tool of the trade to attack the problem. Combining diverse paradigms poses challenging problems of computational and methodological interfacing of several previously incompatible approaches. This is, thus, the setting of HAIS conference series, and its increasing success is the proof of the vitality of this exciting field.

th The 5 International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) has become a unique, established and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques, and bringing the most relevant achievements in this field. Overcoming the rigid encasing imposed by the arising orthodoxy in the field of arti- cial intelligence, which has led to the partition of researchers into so-called areas or fields, interest in hybrid intelligent systems is growing because they give freedom to design innovative solutions to the ever-increasing complexities of real-world pr- lems. Noise and uncertainty call for probabilistic (often Bayesian) methods, while the huge amount of data in some cases asks for fast heuristic (in the sense of suboptimal and ad-hoc) algorithms able to give answers in acceptable time frames. High dim- sionality demands linear and non-linear dimensionality reduction and feature extr- tion algorithms, while the imprecision and vagueness call for fuzzy reasoning and linguistic variable formalization. Nothing impedes real-life problems to mix diffic- ties, presenting huge quantities of noisy, vague and high-dimensional data; therefore, the design of solutions must be able to resort to any tool of the trade to attack the problem. Combining diverse paradigms poses challenging problems of computational and methodological interfacing of several previously incompatible approaches. This is, thus, the setting of HAIS conference series, and its increasing success is the proof of the vitality of this exciting field.