This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We focus on the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF significantly outperform NNet and NB when the number of positive training instances per category are small (less than ten), and that all the methods perform comparably when the categories are sufficiently common (over 300 instances).

This paper analyzes a recently published algorithm for page replacement in hierarchical paged memory systems [OOW93]. The algorithm is called the LRU-K method, and reduces to the well-known LRU (Least Recently Used) method for K=1. Previous work [OOW93, WHMZ94, JS94] has shown the effectiveness for K > 1 by simulation, especially in the most common case of K = 2. The basic idea in LRU-K is to keep track of the times of the last K references to memory pages, and to use this statistical information to rank-order the pages as to their expected future behavior. Based on this the page replacement policy decision is made: which memory-resident page to replace when a newly accessed page must be read into memory. In the current paper we prove, under the assumptions of the independent reference model, that LRU-K is optimal among all replacement algorithms that can be based on information about the K most recent references to each page. The proof uses the Bayesian formula to relate the space of ...

Abstract—Images captured with a typical endoscope show spatial distortion, which necessitates distortion correction for subsequent analysis. In this paper, a new methodology based on least squares estimation is proposed to correct the nonlinear distortion in the endoscopic images. A mathematical model based on polynomial mapping is used to map the images from distorted image space onto the corrected image space. The model parameters include the polynomial coefficients, distortion center, and corrected center. The proposed method utilizes a line search approach of global convergence for the iterative procedure to obtain the optimum expansion coefficients. A new technique to find the distortion center of the image based on curvature criterion is presented. A dual-step approach comprising token matching and integrated neighborhood search is also proposed for accurate extraction of the centers of the dots contained in a rectangular grid, used for the model parameter estimation. The model parameters were verified with different grid patterns. The distortion-correction model is applied to several gastrointestinal images and the results are presented. The proposed technique provides high-speed response and forms a key step toward online camera calibration, which is required for accurate quantitative analysis of the images. Index Terms—Camera calibration, distortion correction, endoscopy, expansion polynomial. I.

Traffic measurement and monitoring are an important component of network management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement and monitoring are not only desirable, but also increasingly becoming a necessity. Since the utility of sampling depends on the accuracy and economy of measurement, it is important to control sampling error. In this paper we propose and analyze an adaptive, stratified random packet sampling technique for flow-level traffic measurement. In particular, we address the theoretical and practical issues involved. Through theoretical studies and experiments, we demonstrate that the proposed sampling technique provides unbiased estimation of flow size with controllable error bound, in terms of both packet and byte counts for elephant flows, while avoiding excessive oversampling. I.

A substantial portion of E. Lutwak's dual Brunn-Minkowski theory, originally applicable only to star-shaped sets, is extended to the class of bounded Borel sets. The extension is motivated by an important application to local stereology, a collection of stereological designs based on sections through a xed reference point that has achieved signi cant medical results in neuroscience and cancer grading.

MOBILE ROBOT LOCALIZATION USING PATTERN CLASSIFICATION TECHNIQUES By Jonathan D. Courtney This thesis describes a methodology for coarse position estimation of a mobile robot within an indoor setting. The approach is divided into two phases: exploration and navigation. In the exploration phase, the mobile robot is allowed to sense the environment for the purpose of mapping and thus "learning" its unknown surroundings. In the navigation phase, the robot senses the surroundings and compares this information with its learned maps for the purpose of locating itself in the workspace. We solve the task of coarse-level mobile robot localization via pattern classification of grid-based maps of important or interesting workspace regions. Using datasets representing 10 different rooms and doorways, we estimate a 94% recognition rate of the rooms and a 98% recognition rate of the doorways. We conclude that coarse position estimation is possible through classification of grid-based maps in indoor...

The research in the paper is motivated by building a decentralized /P2P XML storage on top of a DHT (Distributed Hash Table).

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