is essential. (iv) Relearning after damage is much faster than learning during the original training. (v) Distributed or spaced practice is more effective for long-term retention than massed practice. Network models can be constructed that have the same performance and learning characteristics on a particular

space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When used with multiple generalizers, stacked generalization can be seen as a more

We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting with a simple Xbar grammar, we learn a new grammar whose nonterminals are subsymbols of the original nonterminals

Recent work has shown that combining multiple versions of unstable classifiers such as trees or neural nets results in reduced test set error. One of the more effective is bagging (Breiman [1996a] ) Here, modified training sets are formed by resampling from the original training set, classifiers

, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9% to 58.3%. Similarly, we

to reflect the relevance of the associations. However, noue of these techniques provides functional information along with the collocation. Also, the results produced often contained improper word associations reflecting some spurious aspect of the training corpus that did not stand for true collocations

achieve faster convergence and better modeling of hierarchical structure than the original one. In particular, over 90 % test set bracketing accuracy was achieved for grammars inferred by our algorithm from a training set of hand-parsed part-of-speech strings for sentences in the Air Travel Information

In this paper we introduce the Minimum Phone Error (MPE) and Minimum Word Error (MWE) criteria for the discriminative train-ing of HMM systems. The MPE/MWE criteria are smoothed ap-proximations to the phone or word error rate respectively. We also discuss I-smoothing which is a novel technique

should be described which were largely responsible for the type of model that ultimately emerged. These concern the settings in which research was required and the training and background experiences of those who participated in the development of the Model. HEALTH SETTINGS During the early 1950s

. In this paper we describe a face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The method consists of two steps: first we project the face image from the original vector space to a face subspace via PCA, second we use LDA to obtain a linear