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Record Nr. |
UNISA996465871503316 |
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Titolo |
Ad-Hoc, Mobile, and Wireless Networks [[electronic resource] ] : 4th International Conference, ADHOC-NOW 2005, Cancun, Mexico, October 6-8, 2005, Proceedings / / edited by Violet R. Syrotiuk, Edgar Chávez |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 |
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Edizione |
[1st ed. 2005.] |
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Descrizione fisica |
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1 online resource (XII, 364 p.) |
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Collana |
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Computer Communication Networks and Telecommunications ; ; 3738 |
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Disciplina |
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Soggetti |
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Computer communication systems |
Software engineering |
Information storage and retrieval |
Application software |
Computers and civilization |
Electrical engineering |
Computer Communication Networks |
Software Engineering |
Information Storage and Retrieval |
Information Systems Applications (incl. Internet) |
Computers and Society |
Communications Engineering, Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Invited Presentations -- Another Look at Dynamic Ad-Hoc Wireless Networks -- Routing in Wireless Networks and Local Solutions for Global Problems -- Contributed Papers -- Equilibria for Broadcast Range Assignment Games in Ad-Hoc Networks -- Efficient Mechanisms for Secure Inter-node and Aggregation Processing in Sensor Networks -- Cluster-Based Framework in Vehicular Ad-Hoc Networks -- Randomized AB-Face-AB Routing Algorithms in Mobile Ad Hoc Networks -- A Multiple Path Characterization of Ad-Hoc Network Capacity -- Increasing the Resource-Efficiency of the CSMA/CA |
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Protocol in Directional Ad Hoc Networks -- Location Tracking in Mobile Ad Hoc Networks Using Particle Filters -- Biology-Inspired Distributed Consensus in Massively-Deployed Sensor Networks -- A Key Management Scheme for Commodity Sensor Networks -- Playing CSMA/CA Game to Deter Backoff Attacks in Ad Hoc Wireless LANs -- Design of a Hard Real-Time Guarantee Scheme for Dual Ad Hoc Mode IEEE 802.11 WLANs -- Selective Route-Request Scheme for On-demand Routing Protocols in Ad Hoc Networks -- Enhancing the Security of On-demand Routing in Ad Hoc Networks -- DPUMA: A Highly Efficient Multicast Routing Protocol for Mobile Ad Hoc Networks -- Efficient Broadcasting in Self-organizing Multi-hop Wireless Networks -- MIMOMAN: A MIMO MAC Protocol for Ad Hoc Networks -- Reed-Solomon and Hermitian Code-Based Scheduling Protocols for Wireless Ad Hoc Networks -- An Intelligent Sensor Network for Oceanographic Data Acquisition -- Performance Analysis of the Hierarchical Layer Graph for Wireless Networks -- Heuristic Algorithms for Minimum Bandwith Consumption Multicast Routing in Wireless Mesh Networks -- Probability Distributions for Channel Utilisation -- Enhanced Power-Aware Routing for Mobile Ad Hoc Networks -- Message Stability and Reliable Broadcasts in Mobile Ad-Hoc Networks -- A Detection Scheme of Aggregation Point for Directed Diffusion in Wireless Sensor Networks -- Stressing is Better Than Relaxing for Negative Cost Cycle Detection in Networks -- Cache Placement in Sensor Networks Under Update Cost Constraint -- A Service Discovery Protocol with Maximal Area Disjoint Paths for Mobile Ad Hoc Networks -- Erratum -- Erratum. |
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2. |
Record Nr. |
UNINA9910276947203321 |
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Autore |
Goodfellow Ian |
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Titolo |
Deep learning / / Ian Goodfellow, Yoshua Bengio and Aaron Courville |
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Pubbl/distr/stampa |
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Cambridge, Massachusetts ; ; London, England : , : The MIT Press, , [2016] |
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©2016 |
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ISBN |
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Descrizione fisica |
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1 online resource (xxii, 775 pages) : illustrations |
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Collana |
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Adaptive computation and machine learning |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references (pages 711-766) and index. |
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Nota di contenuto |
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Introduction -- Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models. |
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Sommario/riassunto |
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts |
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in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. |
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