Описание: This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.
Описание: This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.
Автор: Herrera Название: Multilabel Classification ISBN: 3319411101 ISBN-13(EAN): 9783319411101 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are:
• The special characteristics of multi-labeled data and the metrics available to measure them.
• The importance of taking advantage of label correlations to improve the results.
• The different approaches followed to face multi-label classification.
• The preprocessing techniques applicable to multi-label datasets.
• The available software tools to work with multi-label data.
This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
Описание: When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame.
Автор: Joaquim P. Marques de S?; Lu?s M.A. Silva; Jorge M Название: Minimum Error Entropy Classification ISBN: 3642437427 ISBN-13(EAN): 9783642437427 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains the minimum error entropy (MEE) concept applied to data classification machines. Discusses theoretical results, offers a clustering algorithm using a MEE-like concept, and includes tests, evaluation experiments and comparative applications.
Автор: Suk Jin Lee; Yuichi Motai Название: Prediction and Classification of Respiratory Motion ISBN: 3642415083 ISBN-13(EAN): 9783642415081 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book examines current radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. The proposed method improves treatments by considering breathing pattern for accurate dose calculation.
Автор: Timothy Masters Название: Assessing and Improving Prediction and Classification ISBN: 1484233352 ISBN-13(EAN): 9781484233351 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program.
What You'll Learn
Compute entropy to detect problematic predictors.
Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions.
Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing.
Improve classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling.
Use information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising.
Use Monte-Carlo permutation methods to assess the role of good luck in performance results.
Who This Book is For Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
Автор: Saman K. Halgamuge; Lipo Wang Название: Classification and Clustering for Knowledge Discovery ISBN: 3642065422 ISBN-13(EAN): 9783642065422 Издательство: Springer Рейтинг: Цена: 29209.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees.
Автор: Catarina Silva; Bernadete Ribeiro Название: Inductive Inference for Large Scale Text Classification ISBN: 3642045324 ISBN-13(EAN): 9783642045325 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.
Автор: Catarina Silva; Bernadete Ribeiro Название: Inductive Inference for Large Scale Text Classification ISBN: 3642261345 ISBN-13(EAN): 9783642261343 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.
Автор: Kamran Kiasaleh Название: Biological Signals Classification and Analysis ISBN: 3642548784 ISBN-13(EAN): 9783642548789 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Unlike wireless communication systems, biological entities produce signals with underlying nonlinear, chaotic nature that elude classification using the standard signal processing techniques, which have been developed over the past several decades for dealing primarily with standard communication systems.
Автор: Dimitrios Milioris Название: Topic Detection and Classification in Social Networks ISBN: 3319664131 ISBN-13(EAN): 9783319664132 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Introduction.- Background and Related Work.- Joint Sequence Complexity.- Text Classification via Compressive Sensing.- Extension of Joint Complexity and Compressive Sensing.- Conclusion.
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