9,259 research outputs found
Inducing Features of Random Fields
We present a technique for constructing random fields from a set of training
samples. The learning paradigm builds increasingly complex fields by allowing
potential functions, or features, that are supported by increasingly large
subgraphs. Each feature has a weight that is trained by minimizing the
Kullback-Leibler divergence between the model and the empirical distribution of
the training data. A greedy algorithm determines how features are incrementally
added to the field and an iterative scaling algorithm is used to estimate the
optimal values of the weights.
The statistical modeling techniques introduced in this paper differ from
those common to much of the natural language processing literature since there
is no probabilistic finite state or push-down automaton on which the model is
built. Our approach also differs from the techniques common to the computer
vision literature in that the underlying random fields are non-Markovian and
have a large number of parameters that must be estimated. Relations to other
learning approaches including decision trees and Boltzmann machines are given.
As a demonstration of the method, we describe its application to the problem of
automatic word classification in natural language processing.
Key words: random field, Kullback-Leibler divergence, iterative scaling,
divergence geometry, maximum entropy, EM algorithm, statistical learning,
clustering, word morphology, natural language processingComment: 34 pages, compressed postscrip
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An improved connectionist activation function for energy minimization
Symmetric networks that are based on energy minimization, such as Boltzmann machines or Hopfield nets, are used extensively for optimization, constraint satisfaction, and approximation of NP-hard problems. Nevertheless, finding a global minimum for the energy function is not guaranteed, and even a local minimum may take an exponential number of steps. We propose an improvement to the standard activation function used for such networks. The improved algorithm guarantees that a global minimum is found in linear time for tree-like subnetworks. The algorithm is uniform and does not assume that the network is a tree. It performs no worse than the standard algorithms for any network topology. In the case where there are trees growing from a cyclic subnetwork, the new algorithm performs better than the standard algorithms by avoiding local minima along the trees and by optimizing the free energy of these trees in linear time. The algorithm is self-stabilizing for trees (cycle-free undirected graphs) and remains correct under various scheduling demons. However, no uniform protocol exists to optimize trees under a pure distributed demon and no such protocol exists for cyclic networks under central demon
Covert Perceptual Capability Development
In this paper, we propose a model to develop
robots’ covert perceptual capability using reinforcement learning. Covert perceptual behavior is treated as action selected by a motivational system. We apply this model to
vision-based navigation. The goal is to enable
a robot to learn road boundary type. Instead
of dealing with problems in controlled environments with a low-dimensional state space,
we test the model on images captured in non-stationary environments. Incremental Hierarchical Discriminant Regression is used to
generate states on the fly. Its coarse-to-fine
tree structure guarantees real-time retrieval
in high-dimensional state space. K Nearest-Neighbor strategy is adopted to further reduce training time complexity
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