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Full-Text Articles in Physical Sciences and Mathematics
A Comparison Of Load Balancing Algorithms For Parallel Computations, N. Mansouri, Geoffrey C. Fox
A Comparison Of Load Balancing Algorithms For Parallel Computations, N. Mansouri, Geoffrey C. Fox
Electrical Engineering and Computer Science - Technical Reports
Three physical optimization methods are considered in this paper for load balancing parallel computations. These are simulated annealing, genetic algorithms, and neural networks. Some design choices and the inclusion of additional steps lead to new versions of the algorithms with different solution qualities and execution times. The performances of these versions are critically evaluated and compared for test cases with different topologies and sizes. Orthogonal recursive coordinate bisection is also included in the comparison as a typical simple deterministic method. Simulation results show that the algorithms have diverse properties. Hence, different algorithms can be applied to different problems and requirements. …
An Improved Algorithm For Neural Network Classification Of Imbalanced Training Sets, Rangachari Anand, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
An Improved Algorithm For Neural Network Classification Of Imbalanced Training Sets, Rangachari Anand, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Electrical Engineering and Computer Science - Technical Reports
In this paper, we analyze the reason for the slow rate of convergence of net output error when using the backpropagation algorithm to train neural networks for a two-class problems in which the numbers of exemplars for the two classes differ greatly. This occurs because the negative gradient vector computed by backpropagation for an imbalanced training set does not point initially in a downhill direction for the class with the smaller number of exemplars. Consequently, in the initial iteration, the net error for the exemplars in this class increases significantly. The subsequent rate of convergence of the net error is …
Connectionist Expert System With Adaptive Learning Capability, B. T. Low, Hochung Lui, Ah-Hwee Tan, Hoonheng Teh
Connectionist Expert System With Adaptive Learning Capability, B. T. Low, Hochung Lui, Ah-Hwee Tan, Hoonheng Teh
Research Collection School Of Computing and Information Systems
A neural network expert system called adaptive connectionist expert system (ACES) which will learn adaptively from past experience is described. ACES is based on the neural logic network, which is capable of doing both pattern processing and logical inferencing. The authors discuss two strategies, pattern matching ACES and rule inferencing ACES. The pattern matching ACES makes use of past examples to construct its neural logic network and fine-tunes itself adaptively during its use by further examples supplied. The rule inferencing ACES conceptualizes new rules based on the frequencies of use on the rule-based neural logic network. A new rule could …
Analyzing Images Containing Multiple Sparse Patterns With Neural Networks, Rangachari Anand, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Analyzing Images Containing Multiple Sparse Patterns With Neural Networks, Rangachari Anand, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
College of Engineering and Computer Science - Former Departments, Centers, Institutes and Projects
We have addressed the problem of analyzing images containing multiple sparse overlapped patterns. This problem arises naturally when analyzing the composition of organic macromolecules using data gathered from their NMR spectra. Using a neural network approach, we have obtained excellent results in using NMR data to analyze the presence of various amino acids in protein molecules. We have achieved high correct classification percentages (about 87%) for images containing as many as five substantially distorted overlapping patterns.
An Examination And Analysis Of The Boltzmann Machine, Its Mean Field Theory Approximation, And Learning Algorithm, Vincent Clive Phillips
An Examination And Analysis Of The Boltzmann Machine, Its Mean Field Theory Approximation, And Learning Algorithm, Vincent Clive Phillips
Theses : Honours
It is currently believed that artificial neural network models may form the basis for inte1ligent computational devices. The Boltzmann Machine belongs to the class of recursive artificial neural networks and uses a supervised learning algorithm to learn the mapping between input vectors and desired outputs. This study examines the parameters that influence the performance of the Boltzmann Machine learning algorithm. Improving the performance of the algorithm through the use of a naïve mean field theory approximation is also examined. The study was initiated to examine the hypothesis that the Boltzmann Machine learning algorithm, when used with the mean field approximation, …