Ml-knn: A lazy learning approach to multi-label learning (2007)(About) First identify K nearest neighbors in the training set, then, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance
Label Embedding Trees for Large Multi-Class Tasks (2010)(About) Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose **an algorithm for learning a tree-structure of classifiers** which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose **a method that learns to embed labels in a low dimensional space** that is faster than non-embedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including One-vs-Rest while being orders of magnitude faster.
The Building Blocks of Interpretability(About) > Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space.
Retrofitting Word Vectors to Semantic Lexicons (2014)(About) Graph-based learning technique for using lexical relational resources to obtain higher quality semantic vectors, which we call “retrofitting.” Retrofitting is applied as a post-processing step by running belief propagation on a graph constructed from lexicon-derived relational information to update word vectors. This allows retrofitting to be used on pre-trained word vectors obtained using any vector training model.