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We use well-established results in biological vision to construct a novel vision model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear classifier on these features, our model is relatively simple yet outperforms other models on the(More)
The selection of kernel parameters is an open problem in the training of nonlinear support vector machines. The usual selection criterion is the quotient of the radius of the smallest sphere enclosing the training features and the margin width. Empirical studies on real-world data using Gaussian and polynomial kernels show that the test error due to this(More)
We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results(More)
—Named entity recognition (NER) is the task of segmenting and classifying occurrences of names in text. In NER, local contextual cues provide important evidence, but non-local information from the whole document could also prove useful: for example, it is useful to know that " Mary Kay Inc. " has been mentioned in a document to classify subsequent mentions(More)
—Socialness refers to the ability to elicit social interaction and social links among people. It is a concept often associated with individuals. Although there are tangible benefits in socialness, there is little research in its modeling. In this paper, we study socialness as a property that can be associated with items, beyond its traditional association(More)
Centrality measures are crucial in quantifying the roles and positions of vertices in networks. An important measure is betweenness, which is based on the number of shortest paths that vertices fall on. However, betweenness is computationally expensive to derive, resulting in much research on efficient techniques. We note that in many applications, the key(More)