October 1999A discussion of approaches for determining a reference value in the analysis of key-comparison data*

Abstract — In this paper, we present an Internet-based melanoma screening system that newly supports acral volar lesions. A half of Asian melanomas are from these areas and they show completely different appearance from other lesions. Our screening system is accessible from all over the world and diagnoses dermoscopy images within 3-5 sec based on a neural network classifier for non-acral lesions or newly integrated linear classifier for acral volar lesions. Our system achieves a sensitivity of 85.9 % and a specificity of 86.0 % on a set of 1258 non-acral dermoscopy images and a sensitivity of 93.3 % and a specificity of 91.1 % on a set of 199 acral volar dermoscopy images using a leave-one-out cross-validation. I.

Data visualizatio n has the poE tialto assist humans in analyzing and co prehending large vo umes o data, andto detect patterns, clusters ando utliers that are n o o vi oq using noq graphical f ms o presentatiot Fo this reas oq data visualizatio ns have an impo rtant ro le to play in a diverse range o applied pro blems, including data explo atio n and mining, info rmatio n retrieval, and intelligence analysis.

Abstract—Dimensionality reduction is an essential data preprocessing technique for large-scale and streaming data classification tasks. It can be used to improve both the efficiency and the effectiveness of classifiers. Traditional dimensionality reduction approaches fall into two categories: Feature Extraction and Feature Selection. Techniques in the feature extraction category are typically more effective than those in feature selection category. However, they may break down when processing large-scale data sets or data streams due to their high computational complexities. Similarly, the solutions provided by the feature selection approaches are mostly solved by greedy strategies and, hence, are not ensured to be optimal according to optimized criteria. In this paper, we give an overview of the popularly used feature extraction and selection algorithms under a unified framework. Moreover, we propose two novel dimensionality reduction algorithms based on the Orthogonal Centroid algorithm (OC). The first is an Incremental OC (IOC) algorithm for feature extraction. The second algorithm is an Orthogonal Centroid Feature Selection (OCFS) method which can provide optimal solutions according to the OC criterion. Both are designed under the same optimization criterion. Experiments on Reuters Corpus Volume-1 data set and some public large-scale text data sets indicate that the two algorithms are favorable in terms of their effectiveness and efficiency when compared with other state-of-the-art algorithms. Index Terms—Feature extraction, feature selection, orthogonal centroid algorithm. 1

We propose a setup for an odor communication system. Its di#erent parts are described, and ways to realize them are outlined. Our scheme enables an output device --- the whi#er --- to release an imitation of an odorant read in by an input device --- the sni#er --- upon command. The heart of the system is the novel algorithmic scheme that makes the scheme feasible. We are currently at work researching and developing some of the components that constitute the algorithm, and we hope that the description of the overall scheme in this paper will help to get other groups to join in this e#ort.

To my parents

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The U.S. Environmental Protection Agency (U.S. EPA), through its Office of Research and Development (ORD), funded and managed the research described here under Interagency Agreement number DW1393920801-0 with the U.S. Department of Commerce. It has been subjected to the Agency’s peer and administrative review and has been approved for publication as an EPA document. Acknowledgments Many people contributed to this report, through analyses and interpretation, methods development, and other input to the ReVA program. Specifically we would like to acknowledge the following: ii

Abstract. In this paper we discuss a useful family of graph drawing algorithms, characterized by their ability to draw graphs in one dimension. We define the spe-cial requirements from such algorithms and show how several graph drawing tech-niques can be extended to handle this task. In particular, we suggest a novel op-timization algorithm that facilitates using the Kamada and Kawai model [17] for producing one-dimensional layouts. The most important application of the algo-rithms seems to be in achieving graph drawing by axis separation, where each axis of the drawing addresses different aspects of aesthetics.

In this paper I shall describe the use of artificial neural networks for the classification of subjects based on their conversational speech using a set of linguistic measures with particular reference to the application of this approach in classifying dysphasic patients. These linguistic measures can be applied to the transcribed texts of conversational speech of both normal and dysphasic subjects and will quantify the availability of linguistic features which are dependent on word-frequency. The paper presents the results of a cross-validation study using neural networks and compares them against those obtained by using a linear discriminant analysis on the same data.