In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni--a philosopher and an engineer--argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors--a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.
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讀後感
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看得半懂不懂。需要對statistical learning和某些分析哲學的問題，例如古德曼的歸納難題有一定瞭解後再看，纔能收獲較大。總體上來說感覺太過簡略瞭，不是入門讀物。
评分看得半懂不懂。需要對statistical learning和某些分析哲學的問題，例如古德曼的歸納難題有一定瞭解後再看，纔能收獲較大。總體上來說感覺太過簡略瞭，不是入門讀物。
评分O..K.. explores interesting connections but way too telegraphic
评分Harman算是指引我接觸歸納推理的入門者 但他的基本觀點：induction和deduction同為reasoning是一種範疇錯誤 我從來都不認同
评分看得半懂不懂。需要對statistical learning和某些分析哲學的問題，例如古德曼的歸納難題有一定瞭解後再看，纔能收獲較大。總體上來說感覺太過簡略瞭，不是入門讀物。
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