Incremental Learning for Motion Prediction of Pedestrians and Vehicles, Alejandro Dizan Vasquez Govea

Автор: Alejandro Dizan Vasquez Govea
Название: Incremental Learning for Motion Prediction of Pedestrians and Vehicles
ISBN: 3642263852 ISBN-13(EAN): 9783642263859
Издательство: Springer
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Цена: 104480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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: Haym Hirsh
Название: Incremental Version-Space Merging: A General Framework for Concept Learning
ISBN: 1461288347 ISBN-13(EAN): 9781461288343
Издательство: Springer
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Цена: 107130.00 T
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Описание: dis- sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi- mally consistent hypotheses, even in the presence of certain types of incon- sistencies in the data.

Автор: Haym Hirsh
Название: Incremental Version-Space Merging: A General Framework for Concept Learning
ISBN: 0792391195 ISBN-13(EAN): 9780792391197
Издательство: Springer
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Цена: 121110.00 T
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Описание: One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques- tion that is central to understanding how computers might learn: "how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept?" Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis- sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi- mally consistent hypotheses, even in the presence of certain types of incon- sistencies in the data. More generally, it provides a framework for integrat- ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration.