Efficient parallel learning algorithms for neural networks
Advances in neural information processing systems 1
Back propagation is sensitive to initial conditions
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
On the Problem of Local Minima in Backpropagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the convergence of the back-propagation algorithm
Neural Networks
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Noise reduction in a statistical approach to text categorization
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Second-Order Methods for Neural Networks
Second-Order Methods for Neural Networks
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Reduction for Neural Network Based Text Categorization
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Training Neural Networks with Threshold Activation Functions and Constrained Integer Weights
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Hierarchical document categorization with k-NN and concept-based thesauri
Information Processing and Management: an International Journal
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Fuzzy support vector machine for multi-class text categorization
Information Processing and Management: an International Journal
Text classification: A least square support vector machine approach
Applied Soft Computing
Improving neural networks generalization with new constructive and pruning methods
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - SBRN'02
Beyond TFIDF weighting for text categorization in the vector space model
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
Text categorization based on artificial neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
High-order and multilayer perceptron initialization
IEEE Transactions on Neural Networks
Dynamic tunneling technique for efficient training of multilayer perceptrons
IEEE Transactions on Neural Networks
Deterministic convergence of an online gradient method for BP neural networks
IEEE Transactions on Neural Networks
Text categorization algorithms using semantic approaches, corpus-based thesaurus and WordNet
Expert Systems with Applications: An International Journal
A Bayesian feature selection paradigm for text classification
Information Processing and Management: an International Journal
Attribute selection method based on a hybrid BPNN and PSO algorithms
Applied Soft Computing
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
A generalized cluster centroid based classifier for text categorization
Information Processing and Management: an International Journal
Parallel Training of An Improved Neural Network for Text Categorization
International Journal of Parallel Programming
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This paper proposes new modified methods for back propagation neural networks and uses semantic feature space to improve categorization performance and efficiency. The standard back propagation neural network (BPNN) has the drawbacks of slow learning and getting trapped in local minima, leading to a network with poor performance and efficiency. In this paper, we propose two methods to modify the standard BPNN and adopt the semantic feature space (SFS) method to reduce the number of dimensions as well as construct latent semantics between terms. The experimental results show that the modified methods enhanced the performance of the standard BPNN and were more efficient than the standard BPNN. The SFS method cannot only greatly reduce the dimensionality, but also enhances performance and can therefore be used to further improve text categorization systems precisely and efficiently.