Design improvements in the COLFTAR cooled crystal light-value [i.e. valve] /

"UIUCDCS-R-74-669"

The analysis and compensation of nonlinear sampled-data feedback systems.

"Research was supported by the United States Air Force through the Air Force Office of Scientific Research, Air Research and Development Command."

IBM-7090 programs to compile and modify a nuclear data tape /

"May 31, 1961."

Data systems technician 3 & 2.

Cover title.

Information processing relevant to military command: survey, recommendations and bibliography (final report)

Vol. 2: Bibliography appendix.

Documentation of computer programs and automated data systems : proceedings of a symposium held at the National Bureau of Standards, Gaithersburg, MD, October 12, 1976 /

Mode of access: Internet.

A study of the Air Force maintenance technical data system,

ASTIA document AD 288 636.

Weather forecasting : recommendations to address new weather processing system development risks : report to the Secretary of Commerce /

"B-271478"--P. 1.

Texture analysis and cartographic feature extraction /

"January 1985."

Approaches and Strategy for Cancer Research and Surveillance Data: Integration, Information Pipeline, Data Models, and Informatics Opportunities

Thesis (Ph.D.)--University of Washington, 2016-04

The Role of IL-25 in Colitis-Associated Colon Cancer and Pulmonary Inflammation

Thesis (Ph.D.)--University of Washington, 2016-06

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Responses to a merger: The effects of premerger group status and integration pattern

Adopting a social identity perspective, the research was designed to examine the interplay between premerger group status and integration pattern in the prediction of responses to a merger. The research employed a 2 (status: high versus low) x 3 (integration pattern: assimilation versus integrational equality versus transformation) between-participants factorial design. We predicted that integration pattern and group status would interact such that the responses of the members of high status group would be most positive under conditions of an assimilation pattern, whereas members of low status groups were expected to favour an integration-equality pattern. After working on a task in small groups, group status was manipulated and the groups worked on a second task. The merger was then announced and the integration pattern was manipulated (e.g., in terms of the logo, location, and decision rules). The main dependent variables were assessed after the merged groups had worked together on a third task. As expected, there was evidence that the effects of group status on responses to the merger were moderated by integration pattern. Field data also indicated that both premerger status and perceived integration pattern influenced employee responses to an organisational merger.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.