Final edit with all resources updated:
For a project, I am applying machine learning algorithms for classification.
Challenge: Quite limited labeled data and much more unlabeled data.
Goals:
- Apply semi-supervised classification
- Apply a somehow semi-supervised labeling process (known as active learning)
I've found a lot of information from research papers, like applying EM, Transductive SVM or S3VM (Semi Supervised SVM), or somehow using LDA, etc. Even there are few books on this topic.
Question: Where are the implementations and practical sources?
Final update (based on helps provided by mpiktas, bayer, and Dikran Marsupial)
Semi-supervised learning:
- TSVM: in SVMligth and SVMlin.
- EM Naive Bayes in Python
- EM in LinePipe project
Active learning:
- Dualist: an implementation of active learning with source code on text classification
- This webpage serves a wonderful overview of active learning.
- An experimental Design workshop: here.
Deep learning:
- Introductory video at here.
- General site.
- Stanford Unsupervised Feature Learning and Deep Learning tutorial.