SEMI-SUPERVISED SEQUENCE CLASSIFICATION WITH HMMs

Semi-Supervised Sequence Classification with HMMs

Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervised classification algorithms, based on hidden Markov models (HMMs), to classify sequences. For model-based classification, semisupervised learning amounts to using both labeled and unlabeled data to train model parame...

Semi-Supervised Classification with Universum

The Universum data, defined as a collection of ”nonexamples” that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. In this paper, we address a novel semi-supervised classification problem, called semi-supervised Universum, that can simultaneously utilize the labeled data, unlabel...

Detecting Concept Drift in Data Stream Using Semi-Supervised Classification

Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...

Semi-supervised Sequence Learning

We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms...

Protein Sequence Classification with Bayesian Supervised and Semi - Supervised Learned Classifiers