Author : Baochang Zhang
Publisher : Walter de Gruyter GmbH & Co KG
Page : 152 pages
File Size : 50,42 MB
Release : 2020-07-06
Category : Computers
ISBN : 3110595567
The book provides an up-to-date on machine learning and visual perception, including decision tree, Bayesian learning, support vector machine, AdaBoost, object detection, compressive sensing, deep learning, and reinforcement learning. Both classic and novel algorithms are introduced. With abundant practical examples, it is an essential reference to students, lecturers, professionals, and any interested lay readers.
Author : Alexandros Iosifidis
Publisher : Academic Press
Page : 638 pages
File Size : 39,16 MB
Release : 2022-02-04
Category : Technology & Engineering
ISBN : 0323885721
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Author : Guido Tascini
Publisher : World Scientific
Page : 218 pages
File Size : 24,84 MB
Release : 1996-05-06
Category :
ISBN : 9814547921
As perception stands for the acquisition of a real world representation by interaction with an environment, learning is the modification of this internal representation.This book highlights the relation between perception and learning and describes the influence of the learning in the interaction with the environment.Besides, this volume contains a series of applications of both machine learning and perception, where the former is often embedded in the latter and vice-versa.Among the topics covered, there are visual perception for autonomous robots, model generation of visual patterns, attentional reasoning, genetic approaches and various categories of neural networks.
Author : Bruno Apolloni
Publisher : Springer Science & Business Media
Page : 370 pages
File Size : 25,3 MB
Release : 2005-09-14
Category : Technology & Engineering
ISBN : 9783540265498
This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.
Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
Page : 415 pages
File Size : 46,93 MB
Release : 2014-05-19
Category : Computers
ISBN : 1107057132
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author : Andreas Miroslaus Wichert
Publisher : World Scientific
Page : 641 pages
File Size : 29,84 MB
Release : 2021-01-26
Category : Computers
ISBN : 9811234078
This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.Related Link(s)
Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
Page : 481 pages
File Size : 23,95 MB
Release : 2021-07-21
Category : Computers
ISBN : 1098102339
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Author : Lior Rokach
Publisher : World Scientific
Page : 301 pages
File Size : 27,87 MB
Release : 2019-02-27
Category : Computers
ISBN : 9811201978
This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
Author : Horst Bunke
Publisher : World Scientific
Page : 282 pages
File Size : 23,70 MB
Release : 1994
Category : Computers
ISBN : 9810220464
Interest in the automatic processing and analysis of document images has been rapidly increasing during the past few years. This book addresses the different subfields of document image analysis, including preprocessing and segmentation, form processing, handwriting recognition, line drawing and map processing, and contextual processing.
Author : Melanie Mitchell
Publisher : Bradford Book
Page : 0 pages
File Size : 33,89 MB
Release : 1993
Category : Analogy
ISBN : 9780262515443
The psychologist William James observed that "a native talent for perceiving analogies is... the leading fact in genius of every order." The centrality and the ubiquity of analogy in creative thought have been noted again and again by scientists, artists, and writers, and understanding and modeling analogical thought have emerged as two of the most important challenges for cognitive science.Analogy-Making as Perception is based on the premise that analogy-making is fundamentally a high-level perceptual process in which the interaction of perception and concepts gives rise to "conceptual slippages" which allow analogies to be made. It describes Copycat - a computer model of analogymaking, developed by the author with Douglas Hofstadter, that models the complex, subconscious interaction between perception and concepts that underlies the creation of analogies.In Copycat, both concepts and high-level perception are emergent phenomena, arising from large numbers of low-level, parallel, non-deterministic activities. In the spectrum of cognitive modeling approaches, Copycat occupies a unique intermediate position between symbolic systems and connectionist systems a position that is at present the most useful one for understanding the fluidity of concepts and high-level perception.On one level the work described here is about analogy-making, but on another level it is about cognition in general. It explores such issues as the nature of concepts and perception and the emergence of highly flexible concepts from a lower-level "subcognitive" substrate.Melanie Mitchell, Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, is a Fellow of the Michigan Society of Fellows. She is also Director of the Adaptive Computation Program at the Santa Fe Institute.
