Automatic Control Systems by Richard M. Phelan

By Richard M. Phelan

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8 Comparison of the Related Work with this Monograph The hierarchical models discussed so far cover a wide range of concepts and applications. However, the hierarchical structure is very restricted. Typically, the hierarchies have up to 5 levels and nodes have up to four children, where the children have to lie on the next lower level. Unfortunately, these restrictions lead to inefficient representations, where the maximal reusability of parts and primitives can not be reached. In our hierarchical model the level of an object is directly determined by the number of associated low-level features.

Latent Dirichlet Allocation [17]). The main idea of a topic model is that documents are mixtures of hidden topics and that these topics are probability distributions over words. Since topic models are generative, they can be used to make new documents. For that, we first have to choose a distribution over topics. Then, we use this distribution to choose a topic at random. And finally words are drawn from that topic. Fei-Fei and Perona [129] applied this idea successfully to the learning and recognition of natural scene categories.

Where the convolution operator is defined as (f ∗ g)(xi ) = xj f (xj − xi )g(xj ). Thus especially if ψji and the corresponding convolution kernel becomes large, it makes sense to replace the convolution by a multiplication in the Fourier domain. 2 Nonparametric Belief Propagation Let us now consider the BP algorithm presented in Sec. 1 in the case where the random variables are represented by nonparametric distributions. The nonparametric formulation of the BP algorithm was introduced by Sudderth et al.

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