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Linear Models

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Robotic pilot study for analysing spasticity: clinical data versus healthy controls.

Journal of neuroengineering and rehabilitation
BACKGROUND: Spasticity is a motor disorder that causes significant disability and impairs function. There are no definitive parameters that assess spasticity and there is no universally accepted definition. Spasticity evaluation is important in deter...

The feature selection bias problem in relation to high-dimensional gene data.

Artificial intelligence in medicine
OBJECTIVE: Feature selection is a technique widely used in data mining. The aim is to select the best subset of features relevant to the problem being considered. In this paper, we consider feature selection for the classification of gene datasets. G...

SIM-ELM: Connecting the ELM model with similarity-function learning.

Neural networks : the official journal of the International Neural Network Society
This paper moves from the affinities between two well-known learning schemes that apply randomization in the training process, namely, Extreme Learning Machines (ELMs) and the learning framework using similarity functions. These paradigms share a com...

A non-penalty recurrent neural network for solving a class of constrained optimization problems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map ...

A linear functional strategy for regularized ranking.

Neural networks : the official journal of the International Neural Network Society
Regularization schemes are frequently used for performing ranking tasks. This topic has been intensively studied in recent years. However, to be effective a regularization scheme should be equipped with a suitable strategy for choosing a regularizati...

Finite-time synchronization of fractional-order memristor-based neural networks with time delays.

Neural networks : the official journal of the International Neural Network Society
In this paper, we consider the problem of finite-time synchronization of a class of fractional-order memristor-based neural networks (FMNNs) with time delays and investigated it potentially. By using Laplace transform, the generalized Gronwall's ineq...

Spatially regularized machine learning for task and resting-state fMRI.

Journal of neuroscience methods
BACKGROUND: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades.

Development of sediment load estimation models by using artificial neural networking techniques.

Environmental monitoring and assessment
This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions,...

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

Journal of chromatography. A
The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column....

Deep Neural Networks with Multistate Activation Functions.

Computational intelligence and neuroscience
We propose multistate activation functions (MSAFs) for deep neural networks (DNNs). These MSAFs are new kinds of activation functions which are capable of representing more than two states, including the N-order MSAFs and the symmetrical MSAF. DNNs w...