XP. A Common Lisp Pretty Printing System

XP provides efficient and flexible support for pretty printing in Common Lisp. Its single greatest advantage is that it allows the full benefits of pretty printing to be obtained when printing data structures, as well as when printing program code. XP is efficient, because it is based on a linear time algorithm that uses only a small fixed amount of storage. XP is flexible, because users can control the exact form of the output via a set of special format directives. XP can operate on arbitrary data structures, because facilities are provided for specifying pretty printing methods for any type of object. XP also modifies the way abbreviation based on length, nesting depth, and circularity is supported so that they automatically apply to user-defined functions that perform output ??g., print functions for structures. In addition, a new abbreviation mechanism is introduced that can be used to limit the total numbers of lines printed.

XP. A Common Lisp Pretty Printing System

XP provides efficient and flexible support for pretty printing in Common Lisp. Its single greatest advantage is that it allows the full benefits of pretty printing to be obtained when printing data structures, as well as when printing program code. XP is efficient, because it is based on a linear time algorithm that uses a small fixed amount of storage. XP is flexible, because users can control the exact form of the output via a set of special format directives. XP can operate on arbitrary data structures, because facilities are provided for specifying pretty printing methods for any type of object.

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.