Search results for " machine"
showing 10 items of 1317 documents
Learning spatial filters for multispectral image segmentation.
2010
International audience; We present a novel filtering method for multispectral satel- lite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments car- ried out on multiclass one-against-all classification and tar- get detection show the capabilities of the learned spatial fil- ters.
Support Vector Machine and Kernel Classification Algorithms
2018
This chapter introduces the basics of support vector machine (SVM) and other kernel classifiers for pattern recognition and detection. It also introduces the main elements and concept underlying the successful binary SVM. The chapter starts by introducing the main elements and concept underlying the successful binary SVM. Next, it introduces more advanced topics in SVM for classification, including large margin filtering (LMF), SSL, active learning, and large‐scale classification using SVMs. The LMF method performs both signal filtering and classification simultaneously by learning the most appropriate filters. SSL with SVMs exploits the information contained in both labeled and unlabeled e…
Upport vector machines for nonlinear kernel ARMA system identification.
2006
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…
ORGANIZED LEARNING MODELS (PURSUER CONTROL OPTIMISATION)
1983
Abstract The concept of Organized Learning is defined, and some random models are presented. For Not Transferable Learning, it is necessary to start from an instantaneous learning; by a discrete way, we must form a stochastic model considering the probability of each path; with a continue aproximation, we can study the evolution of the internal state through to consider the relative and absolute probabilities, by means of differential equations systems. For Transferable Learning, the instantaneous learning give us directly the System evolution. So, the Algoritmes for the different models are compared.
Detection of developmental dyslexia with machine learning using eye movement data
2021
Dyslexia is a common neurocognitive learning disorder that can seriously hinder individuals’ aspirations if not detected and treated early. Instead of costly diagnostic assessment made by experts, in the near future dyslexia might be identified with ease by automated analysis of eye movements during reading provided by embedded eye tracking technology. However, the diagnostic machine learning methods need to be optimized first. Previous studies with machine learning have been quite successful in identifying dyslexic readers, however, using contrasting groups with large performance differences between diagnosed and good readers. A practical challenge is to identify also individuals with bord…
Performance improvements of real-time crowd simulations
2010
The current challenge for crowd simulations is the design and development of a scalable system that is capable of simulating the individual behavior of millions of complex agents populating large scale virtual worlds with a good frame rate. In order to overcome this challenge, this thesis proposes different improvements for crowd simulations. Concretely, we propose a distributed software architecture that can take advantage of the existing distributed and multi-core architectures. In turn, the use of these distributed architectures requires partitioning strategies and workload balancing techniques for distributed crowd simulations. Also, these architectures allow the use of GPUs not only fo…
Deep CNN for IIF Images Classification in Autoimmune Diagnostics
2019
The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The class…
Enhanced Mathematical Modelling of Interior Permanent Magnet Synchronous Machine Considering Saturation, Cross-Coupling and Spatial Harmonics effects
2020
The Interior Permanent Magnet Synchronous machine (IPMSM) conventional mathematical model is generally employed to investigate and simulate the IPMSM control and drive system behaviour. However, magnetic nonlinearities and spatial harmonics have a substantial influence on the IPMSM electromagnetic behaviour and performances. In order to simulate the IPMSM real electromagnetic behaviour, this paper describes an enhanced mathematical model that takes into account the saturation, cross-coupling and spatial harmonics effects. This model has been implemented in Matlab®/Simulink environment where the electric and magnetic parameters are derived from FEA investigations and implemented by the use o…
Feedback linearization control of wind turbine equipped with doubly fed induction generator
2017
This paper focuses on several control techniques of a wind turbine of rated power of about 1 MW. In particular, a wind generator equipped with an asynchronous doubly-fed induction machine has been considered and its dynamic model in MATLAB/SIMULINK environment has been implemented. Starting from this model the feedback linearization control has been derived, and several simulations have been carried out, with the aim of compare its dynamic performances with the classical field oriented control, and with the V/f control. The results allow us to conclude that a DFIG controlled by a feedback linearization technique ensures better dynamic performance.
How neurophysiological measures can be used to enhance the evaluation of remote tower solutions
2019
New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study, we aimed to demonstrate: (i) the higher resolution of neurophysiological measures in comparison to subjective ones; and (ii) how the simultaneous employment of neurophysiological measures and behavioral ones could allow a…