Constrained Laplacian Score (CLS) is a recently proposed method for semi-supervised feature selection. It presented an outperforming performance comparing to other methods in the state of the art. This is because CLS exploits both unsupervised and supervised parts of data for selecting the most relevant features. However, the choice of the little supervision information (represented by pairwise...

Hierarchical clustering is a common type of in which the dataset hierarchically divided and represented by dendrogram. Agglomerative Clustering (AHC) hierarchical clusters are created bottom-up. In addition, semi-supervised new method field machine learning, where supervised unsupervised learning combined. performance effectively improved as it uses small amount labelled data to aid learning. M...

This paper proposes a simple yet effective framework for semi-supervised dependency parsing at entire tree level, referred to as ambiguity-aware ensemble training. Instead of only using 1best parse trees in previous work, our core idea is to utilize parse forest (ambiguous labelings) to combine multiple 1-best parse trees generated from diverse parsers on unlabeled data. With a conditional rand...

To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML) form) or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tedious manual work, while unlabeled HTML forms are abundant and easy to obtain. In this rese...

Automatic image annotation consists on automatically labeling images, or image regions, with a pre-defined set of keywords, which are regarded as descriptors of the high-level semantics of the image. In supervised learning, a set of previously annotated images is required to train a classifier. Annotating a large quantity of images by hand is a tedious and time consuming process; so an alternat...

Widespread availability and use of music have made automated audio genre classification an important field of research. Thanks to feature extraction systems, not only music data, but also features for them have become readily available. However, handlabeling of a large amount of music data is time consuming. In this study, we introduce a semi-supervised random feature ensemble method for audio ...

In this paper we introduce a mixed approach for the semi-supervised data problem. Our approach consists of an ensemble unsupervised learning part where the labeled and unlabeled points are segmented into clusters. Continuing, we take advantage of the a priori information of the labeled points to assign classes to clusters and proceed to predicting with the ensemble method new incoming ones. Thu...

In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised, semi-supervised and unsupervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by post processing the rules with partial least squares regression have significantly better prediction performance than ...

V isual object classification and tracking are two of the cardinal problems in computer vision. Both tasks are extremely complicated and far from being solved. Recent advances towards building better detection and tracking systems were mainly achieved by improved representations and applying better learning algorithms. For the learning, usually supervised algorithms are applied which demand lar...