[图书][B] Real life applications of soft computing
Rapid advancements in the application of soft computing tools and techniques have proven
valuable in the development of highly scalable systems. Although many resources on the …
valuable in the development of highly scalable systems. Although many resources on the …
Spiking neuron networks a survey
H Paugam-Moisy - 2006 - infoscience.epfl.ch
Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of
neural networks. They derive their strength and interest from an accurate modelling of …
neural networks. They derive their strength and interest from an accurate modelling of …
[PDF][PDF] Large scale machine learning
R Collobert - 2004 - infoscience.epfl.ch
This thesis aims to address machine learning in general, with a particular focus on large
models and large databases. After introducing the learning problem in a formal way, we first …
models and large databases. After introducing the learning problem in a formal way, we first …
Fuzzy neuro systems for machine learning for large data sets
Artificial Neural Networks have found a variety of applications that cover almost every
domain. The increasing use of Artificial Neural Networks and machine learning has led to a …
domain. The increasing use of Artificial Neural Networks and machine learning has led to a …
Parallel approach for ensemble learning with locally coupled neural networks
C Valle, F Saravia, H Allende, R Monge… - Neural processing …, 2010 - Springer
Ensemble learning has gained considerable attention in different tasks including regression,
classification and clustering. Adaboost and Bagging are two popular approaches used to …
classification and clustering. Adaboost and Bagging are two popular approaches used to …
Parallel development and deployment for machine learning models
B Qin, F Azam, D Malov - US Patent 10,482,389, 2019 - Google Patents
Example systems and methods of developing a learning model are presented. In one
example, a sample data set to train a first learning algorithm is accessed. A number of states …
example, a sample data set to train a first learning algorithm is accessed. A number of states …
Damned: A distributed and multithreaded neural event-driven simulation framework
A Mouraud, D Puzenat, H Paugam-Moisy - arXiv preprint cs/0512018, 2005 - arxiv.org
In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed
both in time and in the network architecture. Since a current feature of SNNs is a low …
both in time and in the network architecture. Since a current feature of SNNs is a low …
A parallel evolving algorithm for flexible neural tree
L Peng, B Yang, L Zhang, Y Chen - Parallel Computing, 2011 - Elsevier
In the past few decades, much success has been achieved in the use of artificial neural
networks for classification, recognition, approximation and control. Flexible neural tree (FNT) …
networks for classification, recognition, approximation and control. Flexible neural tree (FNT) …
Computational grid vs. parallel computer for coarse-grain parallelization of neural networks training
V Turchenko - OTM Confederated International Conferences" On the …, 2005 - Springer
Abstract Development of a coarse-grain parallel algorithm of artificial neural networks
training with dynamic mapping onto processors of parallel computer system is considered in …
training with dynamic mapping onto processors of parallel computer system is considered in …
Distributed processing for modelling real-time multimodal perception in a virtual robot
S Chevallier, H Paugam-Moisy, F Lemaître - PDCN'2005, 2005 - hal.science
Built from a need for modelling cognitive processes, a modular neural network is designed
as the" brain" of a virtual robot moving in a prey-predator environment. The robot decides its …
as the" brain" of a virtual robot moving in a prey-predator environment. The robot decides its …