In this paper, a possible worlds framework for representing general belief change operators is presented. In common with many approaches, an agent’s set of beliefs are specifie...
We present a method to estimate the in/out function of a closed surface represented by an unorganized set of data points. From the in/out function, we compute an approximation of ...
Multidimensional scaling (MDS) is a data analysis technique for representing measurements of (dis)similarity among pairs of objects as distances between points in a low-dimensiona...
In this paper, we propose a novel string-todependency algorithm for statistical machine translation. With this new framework, we employ a target dependency language model during d...
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...