AIMC Topic: Datasets as Topic

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Scaling up graph-based semisupervised learning via prototype vector machines.

IEEE transactions on neural networks and learning systems
When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the un...

Real-time Pedestrian Crossing Recognition for Assistive Outdoor Navigation.

Studies in health technology and informatics
Navigation in urban environments can be difficult for people who are blind or visually impaired. In this project, we present a system and algorithms for recognizing pedestrian crossings in outdoor environments. Our goal is to provide navigation cues ...

Analyzing data from a fuzzy rating scale-based questionnaire. A case study.

Psicothema
BACKGROUND: The fuzzy rating scale was introduced to cope with the imprecision of human thought and experience in measuring attitudes in many fields of Psychology. The flexibility and expressiveness of this scale allow us to properly describe the ans...

Computer-based prediction of mitochondria-targeting peptides.

Methods in molecular biology (Clifton, N.J.)
Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where t...

Feature selection using a neural framework with controlled redundancy.

IEEE transactions on neural networks and learning systems
We first present a feature selection method based on a multilayer perceptron (MLP) neural network, called feature selection MLP (FSMLP). We explain how FSMLP can select essential features and discard derogatory and indifferent features. Such a method...