Search Results

• Proposal of a graph-based method for Positive and Unlabeled Learning that uses graph-based strategies in all steps.

Positive and Unlabeled Learning (PUL) uses unlabeled documents and a few positive documents for retrieving a set of “interest” documents from a text collection. Usually, PUL approaches are based on the vector space model. However, when ...

Many real-world networks display hidden community structures with important potential implications in their dynamics. Many algorithms highly relevant to network analysis have been introduced to unveil community structures. Accurate assessment and ...

Graph-based algorithms have aroused considerable interests in recent years by facilitating pattern recognition and learning via information propagation process through the graph. Here, we propose an unsupervised learning algorithm based on ...

Graph-based semi-supervised learning (SSL) provides a powerful framework for the modeling of manifold structures in high-dimensional spaces. Additionally, graph representation is effective for the propagation of the few initial labels existing in ...

Scalable algorithm based on bipartite graphs to perform transduction learning.Label propagation procedure that uses class information associated with vertices and edges.Better performance than state-of-the-art algorithms based on vector space or ...

Sugarcane is an important product to the Brazilian economy because it is the primary ingredient of ethanol which is used as a gasoline substitute. Sugarcane is affected by many factors which can be modelled in a Bayesian Graph. This paper describes a ...

Semi-supervised learning (SSL) is useful when few labeled and plenty of unlabeled examples are available. This occurs in most of the cases due to labeled instances be difficult, expensive and time consuming to be obtained since human experts are ...

The emergence of social media increases the need for the recognization of social influence mainly motivated by online advertising, political and health campaigns, recommendation systems, epidemiological study, etc. In spreading processes, it is possible ...

This article presents a bipartite graph propagation method to be applied to different tasks in the machine learning unsupervised domain, such as topic extraction and clustering. We introduce the objectives and hypothesis that motivate the use of graph ...

Semi-Supervised Learning (SSL) techniques have become very relevant since they require a small set of labeled data. In this scenario, graph-based SSL algorithms provide a powerful framework for modeling manifold structures in high-dimensional spaces and ...

Link prediction in online social networks is useful in numerous applications, mainly for recommendation. Recently, different approaches have considered friendship groups information for increasing the link prediction accuracy. Nevertheless, these ...

Extraction of protein-protein interactions from scientific papers is a relevant task in the biomedical field. Machine learning-based methods such as kernel-based represent the state-of-the-art in this task. Many efforts have focused on obtaining new ...

Cluster detection methods are widely studied in Propositional Data Mining. In this context, data is individually represented as a feature vector. This data has a natural non-relational structure, but can be represented in a relational form through ...

Usually, algorithms for categorization of numeric data have been applied for text categorization after a preprocessing phase which assigns weights for textual terms deemed as attributes. However, due to characteristics of textual data, some algorithms ...

Cluster in graphs is densely connected group of vertices sparsely connected to other groups. Hence, for prediction of a future link between a pair of vertices, these vertices common neighbors may play different roles depending on if they belong or not ...

Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. It is particularly useful when labeled data is scarce. Cotraining is a widely adopted semi-supervised approach ...

In this work we present an approach to extract and to structure bibliographical references from BibTex files, allowing the identification of the duplicate ones, which can appear slightly different in different files. To deal with this problem, existing ...