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Articles 361 - 375 of 375

Full-Text Articles in Computer Sciences

Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao Jun 2005

Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao

Computer Science Faculty Publications

Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …

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The Edam Project: Mining Atmospheric Aerosol Datasets, Raghu Ramakrishnan, James J. Schauer, Lei Chen, Zheng Huang, Martin M. Shafer, Deborah S. Gross, David R. Musicant Jan 2005

The Edam Project: Mining Atmospheric Aerosol Datasets, Raghu Ramakrishnan, James J. Schauer, Lei Chen, Zheng Huang, Martin M. Shafer, Deborah S. Gross, David R. Musicant

Chemistry Faculty Work

Data mining has been a very active area of research in the database, machine learning, and mathematical programming communities in recent years. EDAM (Exploratory Data Analysis and Management) is a joint project between researchers in Atmospheric Chemistry and Computer Science at Carleton College and the University of Wisconsin-Madison that aims to develop data mining techniques for advancing the state of the art in analyzing atmospheric aerosol datasets. There is a great need to better understand the sources, dynamics, and compositions of atmospheric aerosols. The traditional approach for particle measurement, which is the collection of bulk samples of particulates on filters, …

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Blocking Reduction Strategies In Hierarchical Text Classification, Ee Peng Lim, Aixin Sun, Wee-Keong Ng, Jaideep Srivastava Oct 2004

Blocking Reduction Strategies In Hierarchical Text Classification, Ee Peng Lim, Aixin Sun, Wee-Keong Ng, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

One common approach in hierarchical text classification involves associating classifiers with nodes in the category tree and classifying text documents in a top-down manner. Classification methods using this top-down approach can scale well and cope with changes to the category trees. However, all these methods suffer from blocking which refers to documents wrongly rejected by the classifiers at higher-levels and cannot be passed to the classifiers at lower-levels. We propose a classifier-centric performance measure known as blocking factor to determine the extent of the blocking. Three methods are proposed to address the blocking problem, namely, threshold reduction, restricted voting, and …

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Robust Classification Of Event-Related Potential For Brain-Computer Interface, Manoj Thulasidas Sep 2004

Robust Classification Of Event-Related Potential For Brain-Computer Interface, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the …

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Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting Jul 2004

Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting

Faculty Publications

Classification problems with output class overlap create problems for standard neural network approaches. We present a modification of a simple feed-forward neural network that is capable of learning problems with output overlap, including problems exhibiting hierarchical class structures in the output. Our method of applying weakened implicit negatives to address overlap and ambiguity allows the algorithm to learn a large portion of the hierarchical structure from very incomplete data. Our results show an improvement of approximately 58% over a standard backpropagation network on the hierarchical problem.

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Real-Time Classification Algorithm For Recognition Of Machine Operating Modes By Use Of Self-Organizing Maps, Gancho Vachkov, Yuhiko Kiyota, Koji Komatsu, Satoshi Fujii Jan 2004

Real-Time Classification Algorithm For Recognition Of Machine Operating Modes By Use Of Self-Organizing Maps, Gancho Vachkov, Yuhiko Kiyota, Koji Komatsu, Satoshi Fujii

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper a new algorithm for classification and real-time recognition of different a-priorily assumed operating modes for construction machines is proposed. This algorithm utilizes the effectiveness of the Self-Organizing Maps (SOM) for creating the so called Separation Models, that are able to distinguish each operating mode separately. After training, these models are used in a real-time procedure, which calculates at each sampling time the minimal Euclidean distances from the current data point to a certain node of each SOM. Then the separation model (represented by a respective SOM) that has the least minimal distance to this data point defines …

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Building Decision Tree Classifier On Private Data, Wenliang Du, Zhijun Zhan Jan 2002

Building Decision Tree Classifier On Private Data, Wenliang Du, Zhijun Zhan

Electrical Engineering and Computer Science - All Scholarship

This paper studies how to build a decision tree classifier under the following scenario: a database is vertically partitioned into two pieces, with one piece owned by Alice and the other piece owned by Bob. Alice and Bob want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces to the other party or to any third party. We present a protocol that allows Alice and Bob to conduct such a classifier building without having to compromise their privacy. Our protocol uses an untrusted …

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Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong Sep 2001

Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

This paper addresses breast cancer diagnosis problem as a pattern classification problem. Specifically, the problem is studied using Wisconsin-Madison breast cancer data set. Fuzzy rules are generated from the input-output relationship so that the diagnosis becomes easier and transparent for both patients and physicians. For each class, at least one training pattern is chosen as the prototype, provided (a) the maximum membership of the training pattern is in the given class, and (b) among all the training patterns, the neighborhood of this training pattern has the least fuzzy-rough uncertainty in the given class. Using the fuzzy-rough uncertainty, a cluster is …

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Making Use Of The Most Expressive Jumping Emerging Patterns For Classification, Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao May 2001

Making Use Of The Most Expressive Jumping Emerging Patterns For Classification, Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao

Kno.e.sis Publications

Classification aims to discover a model from training data that can be used to predict the class of test instances. In this paper, we propose the use of jumping emerging patterns (JEPs) as the basis for a new classifier called the JEP-Classifier. Each JEP can capture some crucial difference between a pair of datasets. Then, aggregating all JEPs of large supports can produce a more potent classification power. Procedurally, the JEP-Classifier learns the pair-wise features (sets of JEPs) contained in the training data, and uses the collective impacts contributed by the most expressive pair-wise features to determine the class labels …

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Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik Jan 1999

Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik

Turkish Journal of Electrical Engineering and Computer Sciences

The aim of this study is to classify electromyogram (EMG) signals for controlling multifunction proshetic devices. An artificial neural network (ANN) implementation was used for this purpose. Autoregressive (AR) parameters of $a_1, a_2, a_3, a_4$ and their signal power obtained from different arm muscle motions were applied to the input of ANN, which is a multilayer perceptron. At the output layer, for 5000 iterations, six movements were distinguished at a high accuracy of 97.6%.

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Radial Complexity Estimation For Improved Generalization In Artificial Neural Networks, Lemuel R. Myers Jr. Sep 1998

Radial Complexity Estimation For Improved Generalization In Artificial Neural Networks, Lemuel R. Myers Jr.

Theses and Dissertations

When training an artificial neural network (ANN) for classification using backpropagation of error, the weights are usually updated by minimizing the sum-squared error on the training set. As training ensues, overtraining may be observed as the network begins to memorize the training data. This occurs because, as the magnitude of the weight vector, W, grows, the decision boundaries become overly complex in much the same way as a too-high order polynomial approximation can overfit a data set in a regression problem. Since w grows during standard backpropagation, it is important to initialize the weights with consideration to the importance of …

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Clouds: A Decision Tree Classifier For Large Datasets, Khaled Alsabti, Sanjay Ranka, Vineet Singh Jan 1998

Clouds: A Decision Tree Classifier For Large Datasets, Khaled Alsabti, Sanjay Ranka, Vineet Singh

Electrical Engineering and Computer Science - All Scholarship

Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We present a novel decision tree classifier called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces computation and I/O complexity substantially compared to state of the art classifiers, while maintaining the quality of the generated trees in terms of accuracy …

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Analysis Of Myoelectrical Signals For Building A Dextrous Hand, Christopher T. Creel, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka Jun 1994

Analysis Of Myoelectrical Signals For Building A Dextrous Hand, Christopher T. Creel, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka

Electrical Engineering and Computer Science - Technical Reports

We analyze techniques for myoelectrical signals classification for the purpose of designing a multifunctional prosthetic device for human amputees. The main advantage of our system over existing models is that it is more robust, easier to work with, more general, and efficient enough to run in real time. We achieve this with the help of "Supervised Growing Cell Structures." an artificial neural network model designed by Fritzke [10]. The current paper focuses on the flexion of the index, middle and ring fingers, as these are the most difficult movements to tackle.

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Development Of A Classification System For Computer Viruses In The Ibm Pc Environment Using The Dos Operating System, Hugh R. Browne Jan 1993

Development Of A Classification System For Computer Viruses In The Ibm Pc Environment Using The Dos Operating System, Hugh R. Browne

Theses : Honours

The threat to computers worldwide from computer viruses is increasing as new viruses and variants proliferate. Availability of virus construction tools to facilitate 'customised' virus production and wider use of more sophisticated means of evading detection, such as encryption, polymorphic transformation and memory resident 'stealth' techniques increase this problem. Some viruses employ methods to guard against their own eradication from an infected computer, whilst other viruses adopt measures to prevent disassembly of the virus for examination and analysis. Growth in computer numbers and connectivity provide a growing pool of candidate hosts for infection. Standardised and flexible systems for classification and …

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Reduced Set Of Phages For Typing Salmonellae, Melvin Gershman, George Markowsky Feb 1983

Reduced Set Of Phages For Typing Salmonellae, Melvin Gershman, George Markowsky

Computer Science Faculty Research & Creative Works

A set composed of 27 phages is described for differentiating Salmonella spp. representative of groups A, B, C1, C2, D1, D2, E1, E2, E3, E4, G1, K, and N. All of the 1,245 cultures used in this effort were typable and were differentiated on the basis of the 420 phage patterns observed. All results were reproducible. Characteristic phage patterns were produced by a variety of Salmonella serovars isolated from campus incidents and a number of hospital, family, restaurant, and processing plant outbreaks …

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