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Linear Models

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Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging.

Osteoarthritis and cartilage
OBJECTIVE: The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.

A new robust model of one-class classification by interval-valued training data using the triangular kernel.

Neural networks : the official journal of the International Neural Network Society
A robust one-class classification model as an extension of Campbell and Bennett's (C-B) novelty detection model on the case of interval-valued training data is proposed in the paper. It is shown that the dual optimization problem to a linear program ...

Many regression algorithms, one unified model: A review.

Neural networks : the official journal of the International Neural Network Society
Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since t...

Visualization and Interpretation of Support Vector Machine Activity Predictions.

Journal of chemical information and modeling
Support vector machines (SVMs) are among the preferred machine learning algorithms for virtual compound screening and activity prediction because of their frequently observed high performance levels. However, a well-known conundrum of SVMs (and other...

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and ...

A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data.

Neural computation
Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis, by adjusting synaptic weights according to activi...

A Projection Neural Network for Constrained Quadratic Minimax Optimization.

IEEE transactions on neural networks and learning systems
This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the propose...

Mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks.

ISA transactions
The aim of this manuscript is to investigate the mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks with time-delays. The time-delays are assumed to be interval time-varying and randomly...

Incremental learning for ν-Support Vector Regression.

Neural networks : the official journal of the International Neural Network Society
The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to...

Supervised dictionary learning for inferring concurrent brain networks.

IEEE transactions on medical imaging
Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, G...