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Multiple Classifier Systems ; 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings
These proceedings are a record of the Multiple Classifier Systems Workshop, MCS 2007, held at the Institute of Information Theory and Automation, Czech Academy of Sciences, Prague in May 2007. the workshop achieved its objective of bringing together researchers from diverse communities (neural networks, pattern rec- nition, machine learning and statistics) concerned with this research topic.
Methods of microarray data analysis V
In this volume, all investigators analyzed a single dataset on the lifecycle of the most deadly of malaria parasites, Plasmodium falciparum. The emphasis this year is on the application of novel and existing computational methodologies towards infectious disease. We highlight an introductory chapter by Raphael D. Isokpehi, a leading expert in the field of malaria. Ten of the papers presented at the conference are included, which range from the inference of genetic networks to the analysis of the spatial correlation of array data. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state-of-the-art in microarray data analysis.
Genetic programming IV : Routine human-competitive machine intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law
Encyclopedia of Genetics, Genomics, Proteomics and Informatics
It includes about 56% more words than the 1,392-page 2nd edition of 2003. The number of illustrations increased to almost 2,000 and their quality has improved by design and four colors. Cross-references among entries are expanded. The statements are supported by references; more than 14,000 journal papers and more than 3,000 books are listed. The book includes ~1,800 current databases and web servers. Retractions and corrigenda are pointed out.It covers the basics and the latest in genomics, proteomics, genetic engineering, small RNAs, transcription factories, chromosome territories, stem cells, genetic networks, epigenetics, prions, hereditary diseases, patents, etc. Similar integrated information is not available in textbooks or on the Internet. The journal reviews called it the best, high-quality resource for researchers, instructors and students of basic and applied biology, as well as for physicians and lawyers or even for interested laymen because of the clarity of presentation.
Markov Chains : Models, Algorithms and Applications
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.
