Automatic keyphrase extraction methods have generally taken either supervised or unsupervised approaches. Supervised methods extract keyphrases by using a training document set, thus acquiring knowledge from a global collection of texts. Conversely, unsupervised methods extract keyphrases by determining their relevance in a single-document context, without prior learning. We present a hybrid ke...

Data analysis domain dealing with data exploration, clustering and classification is an important problem in many experiments of astrophysics, computer vision, bioinformatics etc. The field of machine learning is increasingly becoming popular for performing these tasks. In this thesis we deal with machine learning models based on unsupervised and supervised learning algorithms. In unsupervised ...

This paper describes a proposed automatic language accent identification system based on phoneme class trajectory models. Our focus is to preserve discriminant information of the spectral evolution that belong to each accent. Here, we describe two classification schemes based on stochastic trajectory models; supervised and unsupervised classification. For supervised classification, we assume te...

BACKGROUND Few studies have examined the predictors of adherence separately for supervised and unsupervised exercise or in postmenopausal women over an extended time period. Here, we report the predictors of exercise adherence in the Alberta Physical Activity and Breast Cancer Prevention (ALPHA) Trial. METHODS The ALPHA trial randomized 160 postmenopausal women in Calgary and Edmonton, Canada...

We introduce a new paradigm to investigate unsupervised learning, reducing unsupervised learning to supervised learning. Specifically, we mitigate the subjectivity in unsupervised decision-making by leveraging knowledge acquired from prior, possibly heterogeneous, supervised learning tasks. We demonstrate the versatility of our framework via comprehensive expositions and detailed experiments on...

General unsupervised learning is a long-standing conceptual problem in machine learning. Supervised learning is successful because it can be solved by the minimization of the training error cost function. Unsupervised learning is not as successful, because the unsupervised objective may be unrelated to the supervised task of interest. For an example, density modelling and reconstruction have of...

There are mainly two kinds of methods for document-level sentiment classification, unsupervised learning and supervised learning. When ensemble learning is introduced, existing methods only combine unsupervised learning algorithms or supervised learning algorithms. To overcome each other’s flaws, a novel sentiment classification method based on behavior-knowledge space is proposed, in which two...

In this paper we reports unsupervised training experiments we have conducted on large amounts of the English Fisher conversational telephone speech. A great amount of work has been reported on unsupervised training, but the major difference of this work is that we compared behaviors of unsupervised training with supervised training on exactly the same data. This comparison reveals surprising re...