1209241
Spectral Energy Minimization for Semi-supervised Learning
Data mining problems often involve a large amount of unlabeled data and there is often very limited known information on the dataset. In such scenario, semi-supervised learning can often improve classification performance by utilizing unlabeled data for learning. In this paper, we proposed a novel approach to semi-supervised learning as as an optimization of both the classification energy and cluster compactness energy in the unlabeled dataset. The resulting integer programming problem is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.
9838468
A Semi-supervised SVM for Manifold Learning
Many classification tasks benefit from integrating manifold learning with semi-supervised learning. By formulating the learning task in a semi-supervised manner, we propose a novel objective function that combines the manifold consistency of whole dataset with the hinge loss of class label prediction. This formulation results in a SVM-alike task operating on the kernel derived from the graph Laplacian, and is capable of capturing the intrinsic manifold structure of the whole dataset and maximizing the margin separating labelled examples. Results on face and handwritten digit recognition tasks show significant performance gain. The performance gain is particularly impressive when only a small training set is available, which is often the true scenario of many real-world problems
12670883
On Hadamard-Type Output Coding in Multiclass Learning
The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository.
957553
A highly scalable and effective method for metasearch
A metasearch engine is a system that supports unified access to multiple local search engines. Database selection is one of the main challenges in building a large-scale metasearch engine. The problem is to efficiently and accurately determine a small number of potentially useful local search engines to invoke for each user query. In order to enable accurate selection, metadata that reflect the contents of each search engine need to be collected and used. This article proposes a highly scalable and accurate database selection method. This method has several novel features. First, the metadata for representing the contents of all search engines are organized into a single integrated representative. Such a representative yields both computational efficiency and storage efficiency. Second, the new selection method is based on a theory for ranking search engines optimally. Experimental results indicate that this new method is very effective. An operational prototype system has been built based on the proposed approach.
6719824
Performance modeling of multicast groups for multiplayer games in peer-to-peer networks
A computer game is usually considered as a finite state machine. For a distributed multiplayer game, state information has to be exchanged among its players when they move and interact in a virtual space. If the number of players is very large, only a small subset of the game entities, controlled by their players, is interested and forms a multicast group so as to reduce the information dissemination over the network. In this paper, analytical models are proposed for evaluating the performance of multicast groups with multiplayer games in peer-to-peer networks. The system modeling is based on two stochastic processes and their possible solutions in terms of game type, player activity, and entity vision size, mode of user input processing, and grouping strategy. The cost of game operations on consumed resources is introduced to characterize the performance measure. Numerical examples are demonstrated, which provide useful insights of the behaviors of such systems.
9755561
A Treemap-Based Result Interface for Search Engine Users
Search engines and metasearch engines on the World Wide Web typically display representative information such as the result document titles and snippets in a one-dimensional ranked list. Alternatively, a few others such as Clusty and Kartoo cluster their results so that users can, to a certain extent, interact with documents, keywords and/or clusters. The number of documents that can be effectively presented in one screen is usually limited to between 10 and 20. In this paper, we propose a method based on treemap visualization that substantially improve the information compactness. In addition, it provides a few unique post-search interaction methods that enable a user to manage a large number of results. An online prototype system is built. Various experiments are designed and done to evaluate the efficiency, effectiveness and usability.
27854712
Retrieval of Multimedia Web Documents and Removal of Redundant Information
This paper describes a search engine for multimedia web documents and a methodology for removing (partially or totally) redundant information from multiple documents in an effort to synthesize new documents. In this paper, a typical multimedia document contains free text and images and additionally has associating well-structured data. An SQL-like query language, WebSSQL, is proposed to retrieve this type of documents. The main differences between WebSSQL and other proposed SQL extensions for retrieving web documents are that WebSSQL is similarity-based and supports conditions on images. This paper also deals with the detection and removal of redundant information (text paragraphs and images) from multiple retrieved documents. Documents reporting the same or related events and stories may contain substantial redundant information. The removal of the redundant information and the synthesis of these documents into a single document can not only save a user's time to acquire the information but also storage space to archive the data. The methodology reported here consists of techniques for analyzing text paragraphs and images as well as a set of similarity criteria used to detect redundant paragraphs and images. Examples are provided to illustrate these techniques.
7810391
Refinement of Nmr-determined Protein Structures with Database Derived Distance Constraints
The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions.