Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Locality preserving indexing for document representation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A comparative study on text representation schemes in text categorization
Pattern Analysis & Applications
Regularized locality preserving indexing via spectral regression
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A Latent Semantic Indexing-based approach to multilingual document clustering
Decision Support Systems
IEEE Transactions on Knowledge and Data Engineering
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analytical evaluation of term weighting schemes for text categorization
Pattern Recognition Letters
Hi-index | 0.00 |
Text classification has gained importance more than ever in the present day owing to the huge amount of data generated with the advent of technology. There are a numerous well established techniques available to achieve classification. It is difficult to declare an algorithm to be universally efficient over the huge variety of datasets created in real time. In this paper, the existing methods are compared and contrasted based on experimental results. The experiment involves testing a document against the training set created previously. The results show quantitative values of the comparable parameters and hence helpful in the choice of a classification algorithm.