AIMC Topic: Supervised Machine Learning

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Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.

PLoS genetics
Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is cru...

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm.

PloS one
Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis c...

Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications.

Computational intelligence and neuroscience
Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Due to variabilities in fMRI data and the limitation of the collection of human fMRI data, it is not easy to tr...

Mining the literature for genes associated with placenta-mediated maternal diseases.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Automated literature analysis could significantly speed up understanding of the role of the placenta and the impact of its development and functions on the health of the mother and the child. To facilitate automatic extraction of information about pl...

Supervised Learning Methods for Predicting Healthcare Costs: Systematic Literature Review and Empirical Evaluation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
An important informatics tool for controlling healthcare costs is accurately predicting the likely future healthcare costs of individuals. To address this important need, we conducted a systematic literature review and identified five methods for pre...

Exploiting Unlabeled Texts with Clustering-based Instance Selection for Medical Relation Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Classifying relations between pairs of medical concepts in clinical texts is a crucial task to acquire empirical evidence relevant to patient care. Due to limited labeled data and extremely unbalanced class distributions, medical relation classificat...

Visual Kinship Recognition of Families in the Wild.

IEEE transactions on pattern analysis and machine intelligence
We present the largest database for visual kinship recognition, Families In the Wild (FIW), with over 13,000 family photos of 1,000 family trees with 4-to-38 members. It took only a small team to build FIW with efficient labeling tools and work-flow....

Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement.

The Journal of surgical research
BACKGROUND: Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostom...

Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.

Neural networks : the official journal of the International Neural Network Society
Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has s...

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Neural networks : the official journal of the International Neural Network Society
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse bi...