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Aprendizado transdutivo baseado em teoria dainformação e teoria do aprendizado estatístico
(Universidade Federal de Minas GeraisUFMG, 2007-07-13)
The machine learning problem is most frequently proposed and solved under the inductive inference paradigm, based on classical inductive principles such as the Empirical Risk Minimization (ERM) and Structural Risk Minimization ...
A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification
(Association for Computing Machinery - ACMDongguk UniversityGyeongju, 2014-03)
A bipartite heterogeneous network is one of the simplest ways to represent a textual document collection. In such case, the network consists of two types of vertices, representing documents and terms, and links connecting ...
An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning.
(Association for Computing Machinery - ACMNew York, 2014-08)
Unsupervised models can provide supplementary soft constraints to help classify new “target” data because similar instances in the target set are more likely to share the same class label. Such models can also help detect ...
Robust Multi-class Graph Transduction with higher order regularization
(International Neural Network Society - INNSIEEE Computational Intelligence SocietyKillarney, 2015-07)
Graph transduction refers to a family of algorithms that learn from both labeled and unlabeled examples using a weighted graph and scarce label information via regularization or label propagation. A recent empirical study ...
Music classification by transductive learning using bipartite heterogeneous networks
(International Society for Music Information Retrieval - ISMIRTaipei, 2014-10)
The popularization of music distribution in electronic format has increased the amount of music with incomplete metadata. The incompleteness of data can hamper some important tasks, such as music and artist recommendation. ...
Post-processing association rules using networks and transductive learning.
(IEEE Systems, Man, and Cybernetics Society - IEEE SMCWayne State UniversityDetroit, 2014-12)
Association is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task ...
Post-processing association rules using networks and transductive learning
(2014-02-05)
Association is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task ...
Nitric oxide signaling in the retina: What have we learned in two decades?
(2012)
Two decades after its first detection in the retina, nitric oxide (NO) continues to puzzle visual neuroscientists. While its liberation by photoreceptors remains controversial, recent evidence supports three subtypes of ...
Learning Boolean logic models of signaling networks with ASP
(Elsevier Science, 2015-09)
Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, ...
Time series transductive classification on imbalanced data sets: an experimental study
(International Association of Pattern Recognition - IAPRLinköping UniversityLund UniversityUppsala UniversityInstitute of Electrical and Electronics Engineers - IEEEStockholm, 2014-08)
Graph-based semi-supervised learning (SSL) algorithms perform well on a variety of domains, such as digit recognition and text classification, when the data lie on a low-dimensional manifold. However, it is surprising that ...