This paper introduces a model of handling Programming-Variations-in-the-Small (PVITS, see [8]) on the basis of program fragments. An abstract syntax of the language to describe variant configurations is automatically derived from the fragment dependency relation. We generate an interactive user

to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribution for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture

learning Boolean functions, linear-threshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each

This paper studies algorithms based on an incremental dynamic programming abstraction of one of the key issues in understanding the behavior of actor-critic learning systems. The prime example of such a learning system is the ASE/ACE architecture introduced by Barto, Sutton, and Anderson (1983

This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set

refined from data. Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation ", etc. The algorithms are an incremental variant of batch learning algorithms from the literature so can work well

squares problems, such as those arising in the training of neural networks, and it has resulted in a much better practical rate of convergence than the steepest descent method. In this paper, we establish the convergence properties of a number of variants of incremental subgradient methods, including some

, combined with treebank-induced classifiers, can be used to build highly accurate disambiguating parsers, in particular for dependency-based syntactic representations. In this article, we first present a general framework for describing and analyzing algorithms for deterministic incremental dependency

algorithms to three other batch algorithms. Surprisingly, when the total time for incremental generation is used, the simplest and less efficient variant of the incremental algorithms outperforms the batch algorithms in most cases. When only the incremental update time is used, the incremental algorithm

and widely used of such algorithms is currently Locally Weighted Projection Regression (LWPR) which has been shown empirically to be both computationally efficient and sufficiently accurate for a number of applications. While incremental variants of non-linear Bayesian regression models have superior