We introduce a novel approach for detection of objects from aerial images at the level of pixels using semi-supervised learning. Buildings in aerial images are complex 3D objects which are represented by features of different modalities include visual information and 3D height data. Semi-supervised learning is a classification which additional unlabeled data can be used to improve accuracy. Thi...

Various unsupervised and semi-supervised methods have been proposed to tag and parse an unseen language. We explore delexicalized parsing, proposed by (Zeman and Resnik, 2008), and delexicalized tagging, proposed by (Yu et al., 2016). For both approaches we provide a detailed evaluation on Universal Dependencies data (Nivre et al., 2016), a de-facto standard for multi-lingual morphosyntactic pr...

Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visu...

Syndromic Surveillance has been performed using machine learning and other statistical methods to detect disease outbreaks. These methods are largely dependent on the availability of historical data to train the machine learning-based surveillance system. However, relevant training data may differ from region to region due to geographical and seasonal trends, meaning that the syndromic surveill...

This article presents a solution along with experimental results for an application of semi-supervised machine learning techniques and improvement on the SVM (Support Vector Machine) based on geodesic model to build text classification applications for Vietnamese language. The objective here is to improve the semi-supervised machine learning by replacing the kernel function of SVM using geodesi...

This paper presents a novel semisupervised learning algorithm called Active Deep Networks (ADN), to address the semi-supervised sentiment classification problem with active learning. First, we propose the semi-supervised learning method of ADN. ADN is constructed by Restricted Boltzmann Machines (RBM) with unsupervised learning using labeled data and abundant of unlabeled data. Then the constru...

I introduce a novel method for disambiguating word senses using a semisupervised approach. I contrast this method with the current state-of-the-art approaches and show that my approach performs well and could potentially lead to fully unsupervised approaches with high accuracy.1

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...

This study implements a vector space model approach to measure the sentiment orientations of words. Two representative vectors for positive/negative polarity are constructed using high-dimensional vector space in both an unsupervised and a semisupervised manner. A sentiment orientation value per word is determined by taking the difference between the cosine distances against the two reference v...