Titlebook: Artificial Intelligence and Soft Computing; 16th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

cauda-equina

https://doi.org/10.1007/978-1-4302-1050-4ness. Proposed DCT method is used to reduce the size of system which results in faster processing with limited and controlled precision lost. Proposed method is compared to other ones like Fuzzy Systems, Neural Networks, Support Vector Machines, etc. to investigate the ability to solve sample proble

antidote

Antimicrobial

Geometric Structures as Design Approach,onen learning rule is used with random parameters providing different neuron locations. Any new neuron configuration allows us to obtain a new ETSP solution. This new approach to exploring the solution space of the ETSP is easy to implement and suitable for relatively large ETSP problems. Furthermor

ANTIC

克制

四海为家的人

Author Profiling with Classification Restricted Boltzmann Machinesfiling framework with no need for handcrafted features and only minor use of text preprocessing and feature engineering. The classifier achieves competitive results when evaluated with the PAN-AP-13 corpus: 36.59% joint accuracy, 57.83% gender accuracy and 59.17% age accuracy. We also examine the re

Perennial长期的

琐碎

Parallel Levenberg-Marquardt Algorithm Without Error Backpropagationhich will also work for MLP but some cells will stay empty. This approach is based on a very interesting idea of learning neural networks without error backpropagation. The presented architecture is based on completely new parallel structures to significantly reduce a very high computational load of

单纯

Spectral Analysis of CNN for Tomato Disease Identificationresults generated by a specific network without considering how the internal part of the network itself has generated those results. The visualization of the activations and features of the neurons generated by the network can help to determine the best network architecture for our proposed idea. By

宽度

From Homogeneous Network to Neural Nets with Fractional Derivative Mechanismuse of calculus of finite differences proposed by Dudek-Dyduch E. and then developed jointly with Tadeusiewicz R. and others. This kind of neural nets was applied mainly to different features extraction i.e. edges, ridges, maxima, extrema and many others that can be defined with the use of classic d