Although LDA has many successes in dimensionality reduction and data separation, it also has disadvantages, especially the small sample size problem in training data because the "within-class scatter" matrix may not be accurately estimated. Moreover, this algorithm can only operate correctly with labeled data in supervised learning. In practice, data collection is very huge and labeling data re...

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their comb...

For a social robot, the ability of learning tasks via human demonstration is very crucial. But most current approaches suffer from either the demanding of the huge amount of labeled training data, or the limited recognition cabability caused by very domain-specific modeling. This paper puts forward a semi-supervised incremental strategy for the robot to learn the manipulative tasks performed by...

In this work we investigate a weakly-supervised approach to learning facial attributes of humans in video. Given a small set of images labeled with attributes and a much larger unlabeled set of video tracks, we train a classifier to recognize these attributes in video data. We make two contributions. First, we show that training on video data improves classification performance over training on...

In this paper, we propose a method to improve nearest neighbor classification accuracy under a semi-supervised setting. We call our approach GS4 (i.e., Generating Synthetic Samples Semi-Supervised). Existing self-training approaches classify unlabeled samples by exploiting local information. These samples are then incorporated into the training set of labeled data. However, errors are propagate...

We present a great improvement while performing semi-supervised learning tasks from training data sets when only a small fraction of the data pairs is labeled. In particular, we propose a novel decision strategy based on normalized model outputs. We give the explanation why the normalization step helps. The paper compares performances of two popular semi-supervised approaches (Consistency Metho...

Opinion mining is a recent subdiscipline of computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. To aid the extraction of opinions from text, recent work has tackled the issue of determining the orientation of “subjective” terms contained in text, i.e. deciding whether a term that carries opinionated content has a positive or a ...

This work addresses one of the common issues arising when building a speech recognition system within a low-resourced scenario adapting the language model on unlabeled audio data. The proposed methodology makes use of such data by means of semisupervised learning. Whilst it has been proven that adding system-generated labeled data for acoustic modeling yields good results, the benefits of addin...

Multilingual training of neural networks for ASR is widely studied these days. It has been shown that languages with little training data can benefit largely from the multilingual resources for training. The use of unlabeled data for the neural network training in semi-supervised manner has also improved the ASR system performance. Here, the combination of both methods is presented. First, mult...

Global atmospheric circulation models (GCM) typically have three primary algorithmic components: columnar physics, spectral tran-form, and semi-Lagrangian transport. In this study, several varients of a SLT method are studied in the context of test cases for the shallow water equations in spherical geometry. A grid point formulation is used with implicit, semi-implicit or explicit time integrat...