Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Using a generalized instance set for automatic text categorization
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Incorporating Prior Knowledge into Boosting
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Learner: a system for acquiring commonsense knowledge by analogy
Proceedings of the 2nd international conference on Knowledge capture
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Text classification from positive and unlabeled documents
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Incorporating Prior Knowledge into SVM for Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Simpler knowledge-based support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Constructing informative prior distributions from domain knowledge in text classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
An automatically constructed thesaurus for neural network based document categorization
Expert Systems with Applications: An International Journal
Web classification of conceptual entities using co-training
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
There are three common challenges in real-world classification applications, i.e. how to use domain knowledge, how to resist noisy samples and how to use unlabeled data. To address these problems, a novel classification framework called Mutually Beneficial Learning (MBL) is proposed in this paper. MBL integrates two learning steps together. In the first step, the underlying local structures of feature space are discovered through a learning process. The result provides necessary capability to resist noisy samples and prepare better input for the second step where a consecutive classification process is further applied to the result. These two steps are iteratively performed until a stop condition is met. Different from traditional classifiers, the output of MBL consists of two components: a common classifier and a set of rules corresponding to local structures. In application, a test sample is first matched with the discovered rules. If a matched rule is found, the label of the rule is assigned to the sample; otherwise, the common classifier will be utilized to classify the sample. We applied the MBL to online news classification, and our experimental results showed that MBL is significantly better than Naïve Bayes and SVM, even when the data is noisy or partially labeled.