Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the sup...

We apply the technique of supervised discriminant analysis (SDA) for in-flow detection in droplet-based magnetofluidics. Based on the SDA, we successfully discriminate bivariant droplets of different volumes containing different encapsulated magnetic content produced by a GMR-based lab-on-chip platform. We demonstrate that the accuracy of discrimination is superior when the correlation of varia...

In this paper, we present our contribution in SemEval2014 ABSA task, some supervised methods for Aspect-Based Sentiment Analysis of restaurant and laptop reviews are proposed, implemented and evaluated. We focus on determining the aspect terms existing in each sentence, finding out their polarities, detecting the categories of the sentence and the polarity of each category. The evaluation resul...

Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised learning. In doing so, we complement distributionfree analysis by providing an alternative framework to measure the value associated with different l...

Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performance of a learning task, in the sense that fewer labeled training data are needed to achieve a target error bound. However, in other situations unlabeled data do not seem to help. Recent attempts at theoretically charact...

This study proposes a method to characterize the scholar h-index by full-text citation analysis. The method combines the citation context analysis, graph mining, and supervised topic modeling to modify the oversimplified process of citation count, and provides more sophisticated assumptions for the scholar h-index in two aspects: the context of citation and the supervised topic-related measure.

This paper surveys three classes of public domain supervised learning algorihms and performs comparative analysis of their performance against 29 of the University California Irvine machine learning datasets.

We explore the near-synonym lexical choice problem using a novel representation of near-synonyms and their contexts in the latent semantic space. In contrast to traditional latent semantic analysis (LSA), our model is built on the lexical level of co-occurrence, which has been empirically proven to be effective in providing higher dimensional information on the subtle differences among near-syn...

We consider the following problem: Given a set of data and one or more examples of clusters, find a clustering of the whole data set that is consistent with the given clusters. This is essentially a semi-supervised clustering problem, but it differs from previously studied semi-supervised clustering settings in significant ways. Earlier work has shown that none of the existing methods for semi-...

Recent work has shown that simple vector subtraction over word embeddings is surprisingly effective at capturing different lexical relations, despite lacking explicit supervision. Prior work has evaluated this intriguing result using a word analogy prediction formulation and hand-selected relations, but the generality of the finding over a broader range of lexical relation types and different l...