Published by World Scientific Publishing Comp, 2012
ISBN 10: 9814291099 ISBN 13: 9789814291095
Language: English
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Paperback. Condition: new. Paperback. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are introduced in classifier design, face recognition, deep learning, time series recognition, image classification, and object detection. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are in Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Add to basketPaperback. Condition: New. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are introduced in classifier design, face recognition, deep learning, time series recognition, image classification, and object detection.
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Paperback. Condition: New. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are introduced in classifier design, face recognition, deep learning, time series recognition, image classification, and object detection.
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Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032452501 ISBN 13: 9781032452500
Language: English
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Paperback. Condition: new. Paperback. Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book.Key FeaturesReviews recent advances in CNN compression and accelerationElaborates recent advances on binary neural network (BNN) technologiesIntroduces applications of BNN in image classification, speech recognition, object detection, and more Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Add to basketPaperback. Condition: new. Paperback. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are introduced in classifier design, face recognition, deep learning, time series recognition, image classification, and object detection. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are in Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Add to basketPaperback. Condition: Brand New. 1st edition. 200 pages. 9.45x6.69x0.47 inches. In Stock.
Paperback. Condition: New. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are introduced in classifier design, face recognition, deep learning, time series recognition, image classification, and object detection.
Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032452501 ISBN 13: 9781032452500
Language: English
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Add to basketPaperback. Condition: new. Paperback. Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book.Key FeaturesReviews recent advances in CNN compression and accelerationElaborates recent advances on binary neural network (BNN) technologiesIntroduces applications of BNN in image classification, speech recognition, object detection, and more Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by World Scientific Publishing Company, 2012
ISBN 10: 9814291099 ISBN 13: 9789814291095
Language: English
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Published by World Scientific Publishing Company, 2012
ISBN 10: 9814291099 ISBN 13: 9789814291095
Language: English
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Published by World Scientific Publishing Company, 2012
ISBN 10: 9814291099 ISBN 13: 9789814291095
Language: English
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Published by World Scientific Publishing Company, Incorporated, 2012
ISBN 10: 9814291099 ISBN 13: 9789814291095
Language: English
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. xii + 372.