22360460
Entropy-constrained tree-structured vector quantizer design
Current methods for the design of pruned or unbalanced tree-structured vector quantizers such as the generalized Breiman-Friedman-Olshen-Stone (GBFOS) algorithm proposed in 1980 are effective, but suffer from several shortcomings. We identify and clarify issues of suboptimality including greedy growing, the suboptimal encoding rule, and the need for time sharing between quantizers to achieve arbitrary rates. We then present the leaf-optimal tree design (LOTD) method which, with a modest increase in design complexity, alters and reoptimizes tree structures obtained from conventional procedures. There are two main advantages over existing methods. First, the optimal entropy-constrained nearest-neighbor rule is used for encoding at the leaves; second, explicit quantizer solutions are obtained at all rates without recourse to time sharing. We show that performance improvement is theoretically guaranteed. Simulation results for image coding demonstrate that close to 1 dB reduction of distortion for a given rate can be achieved by this technique relative to the GBFOS method.
27853952
A non-greedy approach to tree-structured clustering
Abstract We propose a new interdisciplinary approach for the hard optimization problem of tree-structured clustering, wherein the imposition of structural constraints on the solution drastically reduces the complexity of classifying data. The method, derived with analogy to statistical physics, performs a global optimization over the entire tree. Experimentation with non-trivial examples shows that our approach consistently outperforms greedy methods and avoids local minima that may trap them.
17425751
A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data
We address statistical classifier design given a mixed training set consisting of a small labelled feature set and a (generally larger) set of unlabelled features. This situation arises, e.g., for medical images, where although training features may be plentiful, expensive expertise is required to extract their class labels. We propose a classifier structure and learning algorithm that make effective use of unlabelled data to improve performance. The learning is based on maximization of the total data likelihood, i.e. over both the labelled and unlabelled data subsets. Two distinct EM learning algorithms are proposed, differing in the EM formalism applied for unlabelled data. The classifier, based on a joint probability model for features and labels, is a "mixture of experts" structure that is equivalent to the radial basis function (RBF) classifier, but unlike RBFs, is amenable to likelihood-based training. The scope of application for the new method is greatly extended by the observation that test data, or any new data to classify, is in fact additional, unlabelled data - thus, a combined learning/classification operation - much akin to what is done in image segmentation - can be invoked whenever there is new data to classify. Experiments with data sets from the UC Irvine database demonstrate that the new learning algorithms and structure achieve substantial performance gains over alternative approaches.
9952136
Using a distributed single address space operating system to support modern cluster computing
Recent 64-bit microprocessors have made a huge 18.4 quintillion byte address space potentially available to programs. This has led to the design of operating systems that provide a single virtual address space in which all code and data reside in and across all levels of storage and all nodes of a distributed system. These operating systems, called single address space operating systems (SASOSs), have characteristics that can be used to support modern cluster computing in a distributed system in ways that provide an improved program development environment and higher performance than is available from conventional operating systems. Sombrero, our SASOS design, makes use of its hardware support for object-grained protection, separate thread-related protection domains and implicit protection domain crossing to provide support for modern cluster computing which is not available in SASOSs built on stock processors. Its design, which provides direct system-level support for object-oriented programming, includes a number of features targeted specifically at modern cluster computing.
15032028
Higher-order Horn clauses
A generalization of Horn clauses to a higher-order logic is described and examined as a basis for logic programming. In qualitative terms, these higher-order Horn clauses are obtained from the first-order ones by replacing first-order terms with simply typed λ-terms and by permitting quantification over all occurrences of function symbols and some occurrences of predicate symbols. Several proof-theoretic results concerning these extended clauses are presented. One result shows that although the substitutions for predicate variables can be quite complex in general, the substitutions necessary in the context of higher-order Horn clauses are tightly constrained. This observation is used to show that these higher-order formulas can specify computations in a fashion similar to first-order Horn clauses. A complete theorem-proving procedure is also described for the extension. This procedure is obtained by interweaving higher-order unification with backchaining and goal reductions, and constitutes a higher-order generalization of SLD-resolution. These results have a practical realization in the higher-order logic programming language called λProlog.
711002
Counting Distinct Strings
Abstract. This paper discusses how to count and generate strings that are ``distinct'' in two senses: p -distinct and b -distinct. Two strings x on alphabet A and x' on alphabet A' are said to be p -distinct iff they represent distinct ``patterns''; that is, iff there exists no one—one mapping from A to A' that transforms x into x' . Thus aab and baa are p -distinct while aab and ddc are p -equivalent. On the other hand, x and x' are said to be b -distinct iff they give rise to distinct border (failure function) arrays: thus aab with border array 010 is b -distinct from aba with border array 001 . The number of p -distinct (resp. b -distinct) strings of length n formed using exactly k different letters is the [k,n] entry in an infinite p' (resp. b' ) array. Column sums p[n] and b[n] in these arrays give the number of distinct strings of length n . We present algorithms to compute, in constant time per string, all p -distinct (resp. b -distinct) strings of length n formed using exactly k letters, and we also show how to compute all elements p'[k,n] and b'[k,n] . These ideas and results have application to the efficient generation of appropriate test data sets for many string algorithms.
9016351
A framework for evaluating computer supported collaborative learning
Starting from some central issues of Information and Communications Technology (ICT) in learning and of collaborative learning, this paper postulates a composite framework for evaluating learning environments. Using the framework, one scenario is described and analysed. The framework identifies where ICT in learning (e-learning) may be enhanced and where ICT may be difficult for students. Some conclusions are made relevant to the design of ICT learning environments. Areas for future research are suggested.
21145709
A Hybrid Genetic Algorithm for the Single Machine Scheduling Problem
A hybrid genetic algorithm (HGA) is proposed for the single machine, single stage, scheduling problem in a sequence dependent setup time environment within a fixed planning horizon (SSSDP). It incorporates the elitist ranking method, genetic operators, and a hill-climbing technique in each searching area. To improve the performance and efficiency, hill climbing is performed by uniting the Wagner-Whitin Algorithm with the problem-specific knowledge. The objective of the HGA is to minimize the sum of setup cost, inventory cost, and backlog cost. The HGA is able to obtain a superior solution, if it is not optimal, in a reasonable time. The computational results of this algorithm on real life SSSDP problems are promising. In our test cases, the HGA performed up to 50% better than the Just-In-Time heuristics and 30% better than the complete batching heuristics.