Recently, image categorization has been an active research topic due to the urgent need to retrieve and browse digital images via semantic keywords. This paper formulates image categorization as a multi-label classification problem using recent advances in matrix completion. Under this setting, classification of testing data is posed as a problem of completing unknown label entries on a data ma...

In this work, we are concerned with a coarse grained semantic analysis over sparse data, which labels all nouns with a set of semantic categories. To get the benefit of unlabeled data, we propose a bootstrapping framework with Maximum Entropy modeling (MaxEnt) as the statistical learning component. During the iterative tagging process, unlabeled data is used not only for better statistical esti...

This study investigated the impact of semantic clustering on Iranian EFL learners’ vocabulary retention at advanced level. Participants were female learners randomly assigned to two groups of 15. Four instruments (TOEFL test; vocabulary pretest; immediate posttest, and delayed recall posttest) were used. The experimental group underwent semantic clustering vocabulary presentation in which the l...

studies pertaining convergence and its benefits for developing and underdeveloped countries, specifically latin american countries and the caribbean islands are essential because they can create the necessary grounds for further growth and development. the aim of this study is to analyze the economic growth of latin american countries and the caribbeans after their beta and sigma convergences d...

This paper shows how to build a scalable, robust and efficient distributed Internet-scale RDF repository, that we name PAGE (Put And Get Everywhere). 1 Motivation In the recent years, the RDF (Resource Description Framework) data model is gaining momentum. Among other significant examples, take for istance the adoption of key players such as Adobe (i.e., the eXtensible Metadata Platform XMP) an...

This paper introduces a framework for a knowledge-based memetic algorithm, called KBMA. The problem of stock classification is the test bed for the performance of KBMA. Domain knowledge is incorporated into the initialization and reproduction phases of evolutionary computation. In particular, the structure of financial statements is used to sort the attributes, which contributed to a faster con...

Much of the Semantic Web relies upon open and unhindered interoperability between diverse systems; the successful convergence of multiple ontologies and referencing schemes is key. However, this is hampered by the difficult problem of coreference, which is the occurrence of multiple or inconsistent identifiers for a single resource. This paper investigates the origins of this phenomenon and how...

The paper focuses on the description of the approach, taken within the ESPRIT BRA project ACQUILEX, towards: i) acquisition of semantic information from several machinereadable dictionaries (in four languages), and ii) its representation in a common Lexical Knowledge Base. Knowledge extraction is guided by a) empirical observations and b) theoretical hypotheses. As for representation, we stress...

In this work, we show that saturating output activation functions, such as the softmax, impede learning on a number of standard classification tasks. Moreover, we present results showing that the utility of softmax does not stem from the normalization, as some have speculated. In fact, the normalization makes things worse. Rather, the advantage is in the exponentiation of error gradients. This ...

$top$-filters can be used to define $top$-convergence spaces in the lattice-valued context. Connections between $top$-convergence spaces and lattice-valued convergence spaces are given. Regularity of a $top$-convergence space has recently been defined and studied by Fang and Yue. An equivalent characterization is given in the present work in terms of convergence of closures of $top$-filters.  M...