Advanced Structured Prediction (Neural Information Processing series)
暫譯: 進階結構化預測（神經資訊處理系列）

出版商: MIT
出版日期: 2014-12-05
售價: $2,710
貴賓價: 9.5 折 $2,575
語言: 英文
頁數: 432
裝訂: Hardcover
ISBN: 0262028379
ISBN-13: 9780262028370

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商品描述

The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components.

These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England. Peter V. Gehler is a Senior Researcher in the Perceiving Systems group at the Max Planck Institute for Intelligent Systems, Tübingen, Germany. Jeremy Jancsary is a Senior Research Scientist at Nuance Communications, Vienna. Christoph H. Lampert is Assistant Professor at the Institute of Science and Technology Austria, where he heads a group for Computer Vision and Machine Learning.

Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

商品描述(中文翻譯)

結構預測的目標是建立機器學習模型，以預測具有結構的關聯資訊，例如由多個相互關聯的部分組成的資訊。這些模型反映了先前的知識、特定任務的關係和約束，並應用於包括計算機視覺、語音識別、自然語言處理和計算生物學等領域。它們可以執行如預測自然語言句子或將圖像分割成有意義的組件等任務。

這些模型表達能力強且功能強大，但精確計算往往是不可行的。近年來，廣泛的研究努力旨在設計結構預測模型以及計算效率高的近似推理和學習程序。本書提供了這些近期研究的概述，以使這些工作能夠被更廣泛的研究社群所接受。各章節由該領域的領先研究人員撰寫，涵蓋了一系列主題，包括研究趨勢、線性規劃鬆弛方法、概率建模的創新、近期的理論進展以及資源感知學習。

Sebastian Nowozin 是微軟研究院劍橋分院機器學習與感知小組（MLP）的研究員。Peter V. Gehler 是德國圖賓根馬克斯·普朗克智能系統研究所感知系統小組的高級研究員。Jeremy Jancsary 是奧地利維也納Nuance Communications的高級研究科學家。Christoph H. Lampert 是奧地利科學與技術研究所的助理教授，負責計算機視覺與機器學習小組。

貢獻者包括：Jonas Behr、Yutian Chen、Fernando De La Torre、Justin Domke、Peter V. Gehler、Andrew E. Gelfand、Sébastien Giguère、Amir Globerson、Fred A. Hamprecht、Minh Hoai、Tommi Jaakkola、Jeremy Jancsary、Joseph Keshet、Marius Kloft、Vladimir Kolmogorov、Christoph H. Lampert、François Laviolette、Xinghua Lou、Mario Marchand、André F. T. Martins、Ofer Meshi、Sebastian Nowozin、George Papandreou、Daniel Průša、Gunnar Rätsch、Amélie Rolland、Bogdan Savchynskyy、Stefan Schmidt、Thomas Schoenemann、Gabriele Schweikert、Ben Taskar、Sinisa Todorovic、Max Welling、David Weiss、Thomáš Werner、Alan Yuille、Stanislav Živný。