Add to ORKGProject (M.S., Computer Science) -- California State University, Sacramento, 2010.The intent of this project is to explore whether the Two Dimensional Chunk Extraction algorithm for the Self-Splitting Modular Neural Network is more effective than the original algorithm. This experiment uses the same data and compares the results of the Two Dimensional Chunk Extraction algorithm versus the original partitioning algorithm, which splits only on one dimension. The result is that the Two Dimensional Chunk Extraction algorithm performs less effectively than the original algorithm on a suite of test problems.Computer Scienc
The structure and performance of neural networks are intimately connected, and by use of evolutionar...
Abstract. The neural networks are successfully applied to many applications in different domains. Ho...
Deep learning has recently become a very hot topic in Computer Science. It has invaded many applicat...
Abstract-We observe the effects of a variety of splitting strategies for partitioning the input doma...
Project (M.S., Computer Science)-- California State University, Sacramento, 2010.Self-splitting neur...
Project (M.S., Computer Science)--California State University, Sacramento, 2014.The purpose of this ...
Many constructive learning algorithms have been proposed to find an appropriate network structure fo...
Image segmentation is a popular topic enabled by rapid advances in neural network processing. There...
The problem of extracting more compact rules from a rule-based knowledge base is approached by means...
A relaxed group-wise splitting method (RGSM) is developed and evaluated for channel pruning of deep ...
Algorithms for object extraction using a neural network are proposed. A single neuron (processor) is...
Thesis (Ph.D.)--University of Washington, 2020Neural networks trained by machine learning optimizati...
The main purpose of this paper is to demonstrate the data reduction technique of self-organizing map...
Analysis of methods for optimizing algorithms of functioning of the Kohonen neural networks, self-or...
A novel neural network based method for feature extraction is proposed. The method achieves dimensio...
The structure and performance of neural networks are intimately connected, and by use of evolutionar...
Abstract. The neural networks are successfully applied to many applications in different domains. Ho...
Deep learning has recently become a very hot topic in Computer Science. It has invaded many applicat...
Abstract-We observe the effects of a variety of splitting strategies for partitioning the input doma...
Project (M.S., Computer Science)-- California State University, Sacramento, 2010.Self-splitting neur...
Project (M.S., Computer Science)--California State University, Sacramento, 2014.The purpose of this ...
Many constructive learning algorithms have been proposed to find an appropriate network structure fo...
Image segmentation is a popular topic enabled by rapid advances in neural network processing. There...
The problem of extracting more compact rules from a rule-based knowledge base is approached by means...
A relaxed group-wise splitting method (RGSM) is developed and evaluated for channel pruning of deep ...
Algorithms for object extraction using a neural network are proposed. A single neuron (processor) is...
Thesis (Ph.D.)--University of Washington, 2020Neural networks trained by machine learning optimizati...
The main purpose of this paper is to demonstrate the data reduction technique of self-organizing map...
Analysis of methods for optimizing algorithms of functioning of the Kohonen neural networks, self-or...
A novel neural network based method for feature extraction is proposed. The method achieves dimensio...
The structure and performance of neural networks are intimately connected, and by use of evolutionar...
Abstract. The neural networks are successfully applied to many applications in different domains. Ho...
Deep learning has recently become a very hot topic in Computer Science. It has invaded many applicat...