This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. The 22 revised full papers p...
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ISBN:
(数字)9783642203640
ISBN:
(纸本)9783642203633
This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. The 22 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.
This volume contains the papers presented at the inaugural workshop on Data Mining and Bioinformatics at the 32nd International Conference on Very Large Data Bases (VLDB). The purpose of this workshop was to begin bri...
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ISBN:
(数字)9783540689713
ISBN:
(纸本)9783540689706
This volume contains the papers presented at the inaugural workshop on Data Mining and Bioinformatics at the 32nd International Conference on Very Large Data Bases (VLDB). The purpose of this workshop was to begin bringing - gether researchersfrom database, data mining, and bioinformatics areas to help leverage respective successes in each to the others. We also hope to expose the richness, complexity, and challenges in this area that involves mining very large complex biological data that will only grow in size and complexity as geno- scale high-throughput techniques become more routine. The problems are s- ?ciently di?erent enough from traditional data mining problems (outside of life sciences) that novel approaches must be taken to data mine in this area. The workshop was held in Seoul, Korea, on September 11, 2006. We received 30 submissions in response to the call for papers. Each subm- sion was assigned to at least three members of the Program Committee. The Program Committee discussed the submission electronically, judging them on their importance, originality, clarity, relevance, and appropriateness to the - pected audience. The Program Committee selected 15 papers for presentation. These papers arein the areasof microarraydata analysis, bioinformaticssystem and text retrieval, application of gene expression data, and sequence analysis. Because of the format of the workshop and the high number of submissions, many good papers could not be included.
This book constitutes the proceedings of the 22nd Annual Conference on Research in Computational Molecular Biology, RECOMB 2018, held in Paris, France, in April 2018.;The 16 extended and 22 short abstracts&#...
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ISBN:
(数字)9783319899299
ISBN:
(纸本)9783319899282
This book constitutes the proceedings of the 22nd Annual Conference on Research in Computational Molecular Biology, RECOMB 2018, held in Paris, France, in April 2018.;The 16 extended and 22 short abstracts presented were carefully reviewed and selected from 193 submissions. The short abstracts are included in the back matter of the volume. They report on original research in all areas of computational molecular biology and bioinformatics.
Ziel des Buches ist es, Ingenieuren oder Naturwissenschaftlern die Programmierung als Schlüsselqualifikation mit zahlreichen Anwendungsmöglichkeiten vorzustellen. Der Autor erläutert algorithmische Meth...
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ISBN:
(数字)9783662543047
ISBN:
(纸本)9783662543030
Ziel des Buches ist es, Ingenieuren oder Naturwissenschaftlern die Programmierung als Schlüsselqualifikation mit zahlreichen Anwendungsmöglichkeiten vorzustellen. Der Autor erläutert algorithmische Methoden, die heute hinter digitalen Produkten und Dienstleistungen stehen. Zentrale Anwendungen sind die Künstliche Intelligenz, das Data Mining, die Predictive Analytics, die Industrie 4.0 oder das Internet der Dinge. Die digitale Datenanalyse mit den zugehörigen Algorithmen ist die zentrale Grundlage vieler neuer IT-Technologien. Hierzu werden große Datenmengen verschiedenster Anwendungsfelder verarbeitet und auf gewisse Muster analysiert, um die relevanten Informationen zu extrahieren. Diese sogenannten intelligenten Verfahren sind die Basis für nahezu alle Innovationen in unserer digitalisierten Welt. Mit Hilfe dieser Art der Informationsverarbeitung werden durch die Kombination von mathematischen Modellen und algorithmischen Verfahren neue digitale Geschäftsmodelle erschaffen.
Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establi...
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ISBN:
(数字)9783319327747
ISBN:
(纸本)9783319327730
Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical resul...
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ISBN:
(数字)9783319307176
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples.;This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.;This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledg
Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computerscience, operations rese...
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ISBN:
(数字)9783540744467
ISBN:
(纸本)9783540744450
Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computerscience, operations research, and engineering. To a large degree, this popularity is based on the conceptual simplicity of many SLS methods and on their excellent performance on a wide gamut of problems, ranging from rather abstract problems of high academic interest to the very s- ci?c problems encountered in many real-world applications. SLS methods range from quite simple construction procedures and iterative improvement algorithms to more complex general-purpose schemes, also widely known as metaheuristics, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search and variable neighborhood search. Historically, the development of e?ective SLS algorithms has been guided to a large extent by experience and intuition, and overall resembled more an art than a science. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-speci?c background, and which relies heavily on knowledge from a number of disciplines and areas, including computerscience, operations research, arti?cial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.
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