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.
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
16923777
Identifying redundant search engines in a very large scale metasearch engine context
For a given set of search engines, a search engine is redundant if its searchable contents can be found from other search engines in this set. In this paper, we propose a method to identify redundant search engines in a very large-scale metasearch engine context. The general problem is equivalent to an NP hard problem -- the set-covering problem. Due to the large number of search engines that need to be considered and the large sizes of these search engines, approximate solutions must be developed. In this paper, we propose a general methodology to tackle this problem and within the context of this methodology, we propose several new heuristic algorithms for solving the set-covering problem.
18318137
Automatically Detecting Boolean Operations Supported by Search Engines, Towards Search Engine Query Language Discovery
Each Web search engine provides query language through which it can communicate with its users and retrieve corresponding results to user queries. Supporting Boolean operations is a major characteristic of the query language. In this paper, we propose a novel, fully automatic, query probing based approach to identify what Boolean operations that are supported by a search engines and their corresponding syntaxes. Experiments show high effectiveness and efficiency. Along with this, we also provide a Web application called SE-BOSS (Search Engine Boolean Operation Scanning System) for interested users.
6001645
Towards a highly-scalable and effective metasearch engine
A metasearch engine is a system that supports uni ed 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 eAEciently 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 re ect the contents of each search engine need to be collected and used. In this paper, we propose 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 computation eAEciency and storage eAEciency. Second, our selection method is based on a theory for ranking search engines optimally. Experimental results indicate that this new method is very e ective. An operational prototype system has been built based on the proposed approach.
9180158
Protocol Design of MPEG-4 Media Delivery with Active Networks
Traditionally, the Internet supports best effort service. The quality of service available to real time applications such as the delivery of video and audio is therefore difficult to predict and control. Two new technologies have emerged recently, namely the MPEG-4 standard and active network, for media data compression and data networking, respectively. MPEG-4 uses a content or object-based representation. It supports object-based scalability and, in particular, it allows the modification or editing of objects without having to first decode them. Active network allows program code to be loaded dynamically into network nodes. In this paper, we describe a system for real-time media delivery that exploits the strengths of MPEG-4 media encoding and active networks. It is based on a best-effort transport service. Our system performs congestion avoidance, error concealment, and media scalability.
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.