Handbook on Neural Information Processing
Handbook on Neural Information Processing
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神经信息处理手册
DOI：
10.1007/978-3-642-36657-4
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
L. Jain
中科院分区：
文献类型：
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作者：
M. Bianchini;Marco Maggini;L. Jain
这本手册受到两个基本问题的启发：“智能学习机能被制造出来吗？”和“它们能被应用于解决其他无法解决的问题吗？”对于第一个问题，肯定不存在简单唯一的答案。相反，在过去的三十年里，机器学习方面的大量研究已经成功地回答了许多相关但更具体的问题。换句话说，已经提出了许多能够在特定环境中学习的自动工具。它们没有表现出人类意义上的“智能”行为，但它们肯定可以帮助解决涉及对此类环境的深刻感性理解的问题。因此，第二个问题的答案也是不完全令人满意的，即使许多具有挑战性的问题（在经典算法框架中计算难度太大）实际上可以用机器学习技术来解决。在这种观点下，该手册收集了连接主义中成熟的和新的模型，以及它们的学习范式，并提出了使用简单语言的理论特性和高级应用的深入检查，特别是为非专家量身定制的。这一章和整本书并没有假装详尽无遗，而是描绘了一幅不断发展的连接主义图景，其中神经信息系统正朝着试图保持大部分信息不变并使自己专业化的方向发展，有时基于生物学灵感，以熟练地应对现实世界中困难的应用。
This handbook is inspired by two fundamental questions:” Can intelligent learning machines be built?” and” Can they be applied to face problems otherwise unsolvable?”. A simple unique answer certainly does not exist to the first question. Instead in the last three decades, the great amount of research in machine learning has succeeded in answering many related, but far more specific, questions. In other words, many automatic tools able to learn in particular environments have been proposed. They do not show an” intelligent” behavior, in the human sense of the term, but certainly they can help in addressing problems that involve a deep perceptual understanding of such environments. Therefore, the answer to the second question is also partial and not fully satisfactory, even if a lot of challenging problems (computationally too hard to be faced in the classic algorithmic framework) can actually be tackled with machine learning techniques. In this view, the handbook collects both well-established and new models in connectionism, together with their learning paradigms, and proposes a deep inspection of theoretical properties and advanced applications using a plain language, particularly tailored to non experts. Not pretending to be exhaustive, this chapter and the whole book delineate an evolving picture of connectionism, in which neural information systems are moving towards approaches that try to keep most of the information unaltered and to specialize themselves, sometimes based on biological inspiration, to cope expertly with difficult real–world applications.