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
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Second-Order Methods for Neural Networks
Second-Order Methods for Neural Networks
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
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
Using online linear classifiers to filter spam emails
Pattern Analysis & Applications
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
Catching the Drift: Using Feature-Free Case-Based Reasoning for Spam Filtering
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Latent semantic analysis for text categorization using neural network
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
An automatically constructed thesaurus for neural network based document categorization
Expert Systems with Applications: An International Journal
Hierarchical document categorization with k-NN and concept-based thesauri
Information Processing and Management: an International Journal
High-order and multilayer perceptron initialization
IEEE Transactions on Neural Networks
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Deterministic convergence of an online gradient method for BP neural networks
IEEE Transactions on Neural Networks
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This paper proposes a novel approach for spam filtering based on various semantic similarity measures and an adaptive back propagation neural network (ABPNN). Semantic similarity approach is a promising avenue that addresses the problems for keyword based spam filtering model. In this paper, we propose a new method that integrates three kinds of semantic similarity approaches for spam filtering as a case study of data mining application. First, to construct a latent semantic feature space from training data with a statistical method. Second, to build a corpus based thesaurus by extracting the relationship between words based on its co-occurrence in the documents. Third, to combine the latent semantic feature space with the corpus based thesaurus. Back propagation neural network is one of the efficient approaches for classification. However, the traditional BPNN has the problems of slow learning and easy to trap into a local minimum. In this paper, we adopt an adaptive algorithm to improve the traditional BPNN that can overcome these problems. To investigate the effectiveness of our methods, we conduct extensive experiments on ling-spam, PU1 and PU3 data sets. Experimental results show that the proposed system is able to achieve higher performance, especially for the combination of the hybrid semantic similarity approach and the adaptive back propagation neural network.