306293
A self-organizing semantic map for information retrieval
Self-organizing Semantic Map for Information Retrieval Xia Lin Dagobert Soergel Gary Marchionini College of Library and Information Services University of Maryland College Park, A neural network’s unsupervised learning algorithm, Kohonen’s feature map, is applied to constructing a selforganizing semantic map for information retrieval. The semantic map visualizes semantic relationships between input documents, and has properties of economic representation of data with their interrelationships. The potentials of the semantic map include using the map as a retrieval interface for an online bibliographic system. A prototype system that demonstrates this potential is described.
207195547
Graphical table of contents
This paper proposes a graphical table of contents (GTOC) that is functionally analogous to the table of contents. The proposed GTOC can be generated automatically from the text of documents. It visualizes document contents and relationships to allow easy access of underlying documents. It also provides various interactive tools to let the user explore the documents. Issues of how to generate such GTOC include how documents are indexed and organized, how the organized documents are visualized, and what interactive means are needed to provide necessary functionality of GTOC. These issues are discussed in this paper with a GTOC prototype based on Kohonen’s selforganizing featore map algorithm.
2535403
Visualization for the document space
An information retrieval frame work that promotes graphical displays, and that will make documents in the computer visualizable to the searcher, is described. As examples of such graphical displays, two simulation results of using a Kohonen feature map to generate map displays for information retrieval are presented and discussed. The map displays are a mapping from a high-dimensional document space to a two-dimensional space. They show document relationships by various visual cues, such as dots, links, clusters, and areas, as well as their measurement and spatial arrangement. Using the map displays as an interface for document retrieval systems, the user is provided with richer visual information to support browsing and searching.<<ETX>>
21113165
Graphical representations of electronic search patterns
Increasing numbers of researchers and developers are collecting data that trace the processes of humancomputer interactions. Although a variety of methods are used to collect such data, all researchers share common problems in representing and analyzing continuous streams of data. A set of methods and tools for graphically representing the search patterns of users interacting with a variety of databases and hypertexts is under development as part of ongoing research in electronic information seeking. The methodology described in this article involves defining state spaces of user options within a system, capturing keystrokes or mouse moves as raw data, coding these data into the state spaces, and analyzing user moves through the state space as evidence of goal-directed informationseeking strategies and patterns. A tool for displaying user moves through a state space has been developed and is described. The tool graphically represents data in a variety of formats, allowing human pattern recognition capabilities to be applied for data analysis. Graphs can be represented for individual searches or for multiple searches done by either a single searcher or by several searchers working on the same search task.
32681772
Visual MeSH
Visual MeSH is a graphical interface developed to interact with real world databases such as the National Library of Medicine’s MetaThesaurus and MEDLINE. Visual MeSH makes it easy for the user to browse complex relationships through visual representation of thesaurus structures. It helps the user to select controlled vocabulary terms for online searching in the click-andchoose environment.
18690541
Real-time author co-citation mapping for online searching
Author searching is traditionally based on the matching of name strings. Special characteristics of authors as personal names and subject indicators are not considered. This makes it difficult to identify a set of related authors or to group authors by subjects in retrieval systems. In this paper, we describe the design and implementation of a prototype visualization system to enhance author searching. The system, called AuthorLink, is based on author co-citation analysis and visualization mapping algorithms such as Kohonen's feature maps and Pathfinder networks. AuthorLink produces interactive author maps in real time from a database of 1.26 million records supplied by the Institute for Scientific Information. The maps show subject groupings and more fine-grained intellectual connections among authors. Through the interactive interface the user can take advantage of such information to refine queries and retrieve documents through point-and-click manipulation of the authors' names.
40938143
Automating the analysis of voting systems
Voting is a well-known technique to combine the decisions of peer experts. It is used in fault tolerant applications to mask errors from one or more experts using n-modular redundancy (NMR) and n-version programming. Voting strategies include: majority, weighted voting, plurality; instance runoff voting, threshold voting, and the more general weighted k-out-of-n systems. Before selecting a voting schema for a particular application, we have to understand the various tradeoffs and parameters and how they impact the correctness, reliability, and confidence in the final decision made by a voting system. In this paper, we propose an enumerated simulation approach to automate the behavior analysis of voting schemas with application to majority and plurality voting. We conduct synthetic studies using a simulator that we develop to analyze results from each expert, apply a voting mechanism, and analyze the voting results. The simulator builds a decision tree and uses a depth-first traversal algorithm to obtain the system reliability among other factors. We define and study the following behaviors: 1) the probability of reaching a consensus, "Pc"; 2) reliability of the voting system, "R"; 3) certainly index, "T"; and 4) the confidence index, "C". The parameters controlling the analysis are the number of participating experts, the number of possible output symbols that can be produced by an expert, the probability distribution of each expert's output, and the voting schema. The paper presents an enumerated simulation approach for analyzing voting systems which can be used when the use of theoretical models are challenged by dependencies between experts or uncommon probability distributions of the expert's output.
5163377
Handwritten numeral recognition using MFNN based multiexpert combination strategy
A novel unconstrained numeral recognition system using an MFNN (multilayered feedforward neural net)-based multi-expert combination strategy is proposed. Taking advantage of every method's confidence value as well as its output label, this strategy can improve the system's performance significantly with as few as two experts. Another novel feature of the system is that the multi-expert combination problem is converted into a new classification problem, and then the MFNNs are used to handle the combination adaptively. The proposed system has achieved promising results on the NIST database.
17019459
Semantic video classification by integrating flexible mixture model with adaptive EM algorithm
Digital video now plays an important role in medical education and healthcare, but our ability to automatic video indexing at semantic level is currently primitive. In this paper, we propose a novel framework to enable more effective semantic video classification and indexing in a specific surgery education video domain. Specifically, this framework includes: (a) A novel semantic-sensitive video content characterization and representation framework by using principal video shots and their perceptual multimodal features. (b) A novel semantic medical concept interpretation technique by using flexible mixture model. (c) A semantic video classifier by using an adaptive Expectation-Maximization (EM) algorithm for automatic parameter estimation and model selection (i.e., selecting the optimal number of mixture Gaussian components). Since more effective video content characterization framework has been integrated with an adaptive EM algorithm for video classification, our semantic video classifier has improved the classification accuracy significantly. For skin classification, its accuracy is close to 95.5%. For semantic surgical video classification, it achieves overall ≈ 84.6% accuracy.
19601209
Pricing-Based Control of Large Networks
In this work we study pricing as a mechanism to control large networks. Our model is based on revenue maximization in a general loss network with Poisson arrivals and arbitrary holding time distributions. In dynamic pricing schemes, the network provider can charge different prices to the user according to the current utilization level of the network. We show that, when the system becomes large, the performance of an appropriately chosen static pricing scheme, whose price is independent of the current network utilization, will approach that of the optimal dynamic pricing scheme. Further, we show that under certain conditions, this static price is independent of the route that the connections take. This result has the important implication that distance-independent pricing that is prevalent in current domestic telephone networks in the U.S. may in fact be appropriate not only from a simplicity, but also a performance point of view. We also show that in large systems prior knowledge of the connection holding time is not important from a network revenue point of view.
40737527
On the computational complexity of edge concentration
Suppose that G=(U,L,E) is a bipartite graph with vertex set U∪L and edge set E⊆U×L. A typical convention for drawing G is to put the vertices of U on a horizontal line and the vertices of L on another horizontal line, and then to represent edges by line segments between the vertices that determine them. “Edge concentration” is known as an effective method to draw dense bipartite graphs clearly. The key in the edge concentration method is to reduce the number of edges, while the graph structural information is retained. The problem of having a maximal reduction on the number of edges by the edge concentration method was left open. In this paper we show that this problem is NP-hard.