Adaptation-Based classiefirs for handling some problems with multi-label data/
xi, 189 pages, figs; Content notes : Introduction -- Background -- Autoencoders and Extreme Learning Machines based Multi-label Classiers -- Functional Link Articial Neural Network based Multi-label Classier -- Binary Tree of Classiers for Multi-label Data -- Improved Multi-label Classication with Frequent Label-set Mining and
Association -- Conclusion & Future Scope Deep Learning
