Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Single-shot detection of multiple categories of text using parametric mixture models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Introduction to Data Mining and its Applications (Studies in Computational Intelligence)
Introduction to Data Mining and its Applications (Studies in Computational Intelligence)
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Introduction to Information Retrieval
Introduction to Information Retrieval
Exponential family sparse coding with applications to self-taught learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Self-taught learning
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Cost effective depression patient thought record categorization via self-taught learning
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Dyadic transfer learning for cross-domain image classification
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Personal and Ubiquitous Computing
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Automatic patient thought record categorization (TR) is important in cognitive behavior therapy, which is an useful augmentation of standard clinic treatment for major depressive disorder. Because both collecting and labeling TR data are expensive, it is usually cost prohibitive to require a large amount of TR data, as well as their corresponding category labels, to train a classification model with high classification accuracy. Because in practice we only have very limited amount of labeled and unlabeled training TR data, traditional semi-supervised learning methods and transfer learning methods, which are the most commonly used strategies to deal with the lack of training data in statistical learning, cannot work well in the task of automatic TR categorization. To address this challenge, we propose to tackle the TR categorization problem from a new perspective via self-taught learning, an emerging technique in machine learning. Self-taught learning is a special type of transfer learning. Instead of requiring labeled data from an auxiliary domain that are relevant to the classification task of interest as in traditional transfer learning methods, it learns the inherent structures of the auxiliary data and does not require their labels. As a result, a classifier achieves decent classification accuracy using the limited amount of labeled TR texts, with the assistance from the large amount of text data obtained from some inexpensive, or even no-cost, resources. That is, a cost-effective TR categorization system can be built that may be particularly useful for diagnosis of patients and training of new therapists. By further taking into account the discrete nature input text data, instead of using the traditional Gaussian sparse coding in self-taught learning, we use exponential family sparse coding to better simulate the distribution of the input data. We apply the proposed method to the task of classifying patient homework texts. Experimental results show the effectiveness of the proposed automatic TR classification framework.