Sequential Supervised Learning and Conditional Random Fields

TL;DR: This paper uses frequent sequential pattern mining to extract subcascades as features for cascade outbreak prediction and proposes a max-margin based classifier to select at most B features for prediction.

TL;DR: TreeCRFpsi is the first secondary structure prediction method based on a well-defined probabilistic model, which makes it easier to use the output predictions as inputs to subsequent analysis steps, and is the best among all published results.

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.

TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.