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Condizione: New.

Condizione: New.

Hardcover. Condizione: Brand New. 167 pages. 6.69x0.44x9.61 inches. In Stock.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores various aspects of knowledge graph reasoning to solve different tasks, encompassing first, traditional symbolic methods for knowledge graph reasoning; second, recent developments in neural-based knowledge graph reasoning techniques; and third, cutting-edge advancements in neural-symbolic hybrid approaches to knowledge graph reasoning. The authors focus on the model and algorithm design aspect and study knowledge graphs from two perspectives: background knowledge graph and input query. Knowledge graph reasoning, which aims to infer and discover new knowledge from existing information in the knowledge graph, has played an important role in many real-world applications, such as question answering and recommender systems. A new trend in knowledge graph reasoning is the combination of neural models with symbolic knowledge graphs, allowing for the design of models that are not only efficient and accurate, but also interpretable. In this book, the authors study the application of neural-symbolic knowledge reasoning to different tasks from two perspectives: the input query and the background knowledge graph.

Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

Condizione: New. In.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

Paperback. Condizione: Brand New. 207 pages. 9.25x6.10x0.59 inches. In Stock.

Hardcover. Condizione: Brand New. 207 pages. 9.25x6.10x0.60 inches. In Stock.

Paperback. Condizione: New.

Paperback. Condizione: New.

Paperback. Condizione: New.

Condizione: New.

Paperback. Condizione: New.

Hardcover. Condizione: new. Hardcover. This book explores various aspects of knowledge graph reasoning to solve different tasks, encompassing first, traditional symbolic methods for knowledge graph reasoning; second, recent developments in neural-based knowledge graph reasoning techniques; and third, cutting-edge advancements in neural-symbolic hybrid approaches to knowledge graph reasoning. The authors focus on the model and algorithm design aspect and study knowledge graphs from two perspectives: background knowledge graph and input query. Knowledge graph reasoning, which aims to infer and discover new knowledge from existing information in the knowledge graph, has played an important role in many real-world applications, such as question answering and recommender systems. A new trend in knowledge graph reasoning is the combination of neural models with symbolic knowledge graphs, allowing for the design of models that are not only efficient and accurate, but also interpretable. In this book, the authors study the application of neural-symbolic knowledge reasoning to different tasks from two perspectives: the input query and the background knowledge graph. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores various aspects of knowledge graph reasoning to solve different tasks, encompassing first, traditional symbolic methods for knowledge graph reasoning; second, recent developments in neural-based knowledge graph reasoning techniques; and third, cutting-edge advancements in neural-symbolic hybrid approaches to knowledge graph reasoning. The authors focus on the model and algorithm design aspect and study knowledge graphs from two perspectives: background knowledge graph and input query. Knowledge graph reasoning, which aims to infer and discover new knowledge from existing information in the knowledge graph, has played an important role in many real-world applications, such as question answering and recommender systems. A new trend in knowledge graph reasoning is the combination of neural models with symbolic knowledge graphs, allowing for the design of models that are not only efficient and accurate, but also interpretable. In this book, the authors study the application of neural-symbolic knowledge reasoning to different tasks from two perspectives: the input query and the background knowledge graph. 151 pp. Englisch.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Hardcover. Condizione: new. Hardcover. This book explores various aspects of knowledge graph reasoning to solve different tasks, encompassing first, traditional symbolic methods for knowledge graph reasoning; second, recent developments in neural-based knowledge graph reasoning techniques; and third, cutting-edge advancements in neural-symbolic hybrid approaches to knowledge graph reasoning. The authors focus on the model and algorithm design aspect and study knowledge graphs from two perspectives: background knowledge graph and input query. Knowledge graph reasoning, which aims to infer and discover new knowledge from existing information in the knowledge graph, has played an important role in many real-world applications, such as question answering and recommender systems. A new trend in knowledge graph reasoning is the combination of neural models with symbolic knowledge graphs, allowing for the design of models that are not only efficient and accurate, but also interpretable. In this book, the authors study the application of neural-symbolic knowledge reasoning to different tasks from two perspectives: the input query and the background knowledge graph. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condizione: new. Hardcover. This book explores various aspects of knowledge graph reasoning to solve different tasks, encompassing first, traditional symbolic methods for knowledge graph reasoning; second, recent developments in neural-based knowledge graph reasoning techniques; and third, cutting-edge advancements in neural-symbolic hybrid approaches to knowledge graph reasoning. The authors focus on the model and algorithm design aspect and study knowledge graphs from two perspectives: background knowledge graph and input query. Knowledge graph reasoning, which aims to infer and discover new knowledge from existing information in the knowledge graph, has played an important role in many real-world applications, such as question answering and recommender systems. A new trend in knowledge graph reasoning is the combination of neural models with symbolic knowledge graphs, allowing for the design of models that are not only efficient and accurate, but also interpretable. In this book, the authors study the application of neural-symbolic knowledge reasoning to different tasks from two perspectives: the input query and the background knowledge graph. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores various aspects of knowledge graph reasoning to solve different tasks, encompassing first, traditional symbolic methods for knowledge graph reasoning; second, recent developments in neural-based knowledge graph reasoning techniques; and third, cutting-edge advancements in neural-symbolic hybrid approaches to knowledge graph reasoning. The authors focus on the model and algorithm design aspect and study knowledge graphs from two perspectives: background knowledge graph and input query. Knowledge graph reasoning, which aims to infer and discover new knowledge from existing information in the knowledge graph, has played an important role in many real-world applications, such as question answering and recommender systems. A new trend in knowledge graph reasoning is the combination of neural models with symbolic knowledge graphs, allowing for the design of models that are not only efficient and accurate, but also interpretable. In this book, the authors study the application of neural-symbolic knowledge reasoning to different tasks from two perspectives: the input query and the background knowledge graph.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 172 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three k.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning.The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and impleme.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.