Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Natacha Gueorguieva"'
Publikováno v:
Procedia Computer Science. 185:223-230
Convolutional neural networks encounter exploding/vanishing gradient and higher training error which cannot be overcome by adding layers, enhancing initialization methods, and better optimizers. Residual Neural Networks (ResNet) include added layers
Publikováno v:
Procedia Computer Science. 168:26-33
Development of efficient neural network classifier topology involves preprocessing of training data to reduce noise and discrepancies, utilizing a number of hidden layers, choice of correct activation functions etc. In this research we propose using
Publikováno v:
World Congress on Electrical Engineering and Computer Systems and Science.
Publikováno v:
World Congress on Electrical Engineering and Computer Systems and Science.
Publikováno v:
Procedia Computer Science. 114:224-233
The proposed modification of conventional fuzzy C-means clustering (FCM) algorithm aims to correct some of its shortcomings. We have focused on as missing flexibility in cluster number adaptation; limited cluster type grouping; less than optimal obje
Publikováno v:
NER
Most of the regression-based Deep Learning (DL) algorithms which were recently proposed are based on Convolutional Neural Networks (CNN) trained by using l 2 loss function. In order to avoid the vulnerability of it to outliers, some authors propose t
Publikováno v:
Complex Adaptive Systems
Information processing in the brain results from the spread and interaction of electrical and chemical signals among neurons. The Hodgkin-Huxley model, describes the spiking and refractory properties of real neurons and serves as a paradigm based on
Publikováno v:
Complex Adaptive Systems
In this paper we present analysis and solutions to problems related to initial positioning of neurons in a classic self-organizing map (SOM) neural network. This means that we are not concerned with the multitude of growing variants, where new neuron
Publikováno v:
Complex Adaptive Systems
Neurons communicate via electrochemical currents, thus simulation is typically accomplished through modeling the dynamical nature of the neuron's electrical properties. In this paper we utilize Hodgkin-Huxley model and briefly compare it to Leaky int
Publikováno v:
FUZZ-IEEE
Principal component analysis (PCA) extracts small uncorrelated data from original high dimensional data space and is widely used for data analysis. The methodology of classical PCA is based on orthogonal projection defined in convex vector space. Thu