Titlebook: Hormonal Carcinogenesis V; Jonathan J. Li,Sara A. Li,Thierry Maudelonde Book 2008 The Editor(s) (if applicable) and The Author(s), under e

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Michael F. Clarke solving tasks such as puzzles and chess to perceptual tasks such as speech and vision, the problem characteristics change dramatically: knowledge poor to knowledge rich; low data rates to high data rates; slow response time (minutes to hours) to instantaneous response time. These characteristics ta

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Bryan T. Hennessy,Mandi Murph,Meera Nanjundan,Mark Carey,Nelly Auersperg,Jonas Almeida,Kevin R. Coommixture models, and extends the reviews to the Markov chain and the hidden Markov sequence or model (HMM). Central to the HMM is the concept of state, which is itself a random variable typically taking discrete values. Extending from a Markov chain to an HMM involves adding uncertainty or a statisti

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David J. Mulholland,Jing Jiao,Hong Wuo the structure of internal states or memory in the RNN, endowing it with the dynamic temporal behavior not exhibited by the DNN discussed in earlier chapters. In this chapter, we first present the state-space formulation of the basic RNN as a nonlinear dynamical system, where the recurrent matrix g

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Vanessa Duong,Patrick Augereau,Eric Badia,Stéphan Jalaguier,Vincent Cavailles us to use ever more complex algorithms in server-based large vocabulary ASR systems (e.g. in telephony applications), the capability of embedded platforms will always lag behind. Nevertheless as the popularity of ASR application grows, we can expect an increasing demand for functionality on embedde