clustering neural network approaches de srivastava chandan (5 resultados)

Condición: New.

Taschenbuch. Condición: Neu. Neuware -This book is based on the basis that clustering and neural networks methods. The clustering algorithms (k-means, and k-medoids) are describe the analysis on noise data i.e. preclassification for robust model development. The better choice of cluster are forming by Euclidean statistical clustering algorithms, are able to preclassified data into significant groups. We assume that both methods are better predicted on different example in real analysis. The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the best optimal result. Since we have input unit, output unit, and eventually hidden unit; we could say that this is supervised optimization learning process. The optimization process to minimizing the error and get the activated network till that all weight of the network are going to reach equilibrium state). This process usually take more time because every output of network add-up with input again and train network (weight) to reach the equilibrium state (optimal solution).Reported results and graphical user interface (GUI) snapshot, showing algorithms are integrated well in software package (simulator).Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch.

Taschenbuch. Condición: Neu. Clustering and Neural Network Approaches for General NN-Simulator | K-means, K-mediods, Recurrent Backpropagation,and Artificial Neural Network Simulator | Chandan Srivastava | Taschenbuch | 68 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845409429 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is based on the basis that clustering and neural networks methods. The clustering algorithms (k-means, and k-medoids) are describe the analysis on noise data i.e. preclassification for robust model development. The better choice of cluster are forming by Euclidean statistical clustering algorithms, are able to preclassified data into significant groups. We assume that both methods are better predicted on different example in real analysis. The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the best optimal result. Since we have input unit, output unit, and eventually hidden unit; we could say that this is supervised optimization learning process. The optimization process to minimizing the error and get the activated network till that all weight of the network are going to reach equilibrium state). This process usually take more time because every output of network add-up with input again and train network (weight) to reach the equilibrium state (optimal solution).Reported results and graphical user interface (GUI) snapshot, showing algorithms are integrated well in software package (simulator). 68 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is based on the basis that clustering and neural networks methods. The clustering algorithms (k-means, and k-medoids) are describe the analysis on noise data i.e. preclassification for robust model development. The better choice of cluster are forming by Euclidean statistical clustering algorithms, are able to preclassified data into significant groups. We assume that both methods are better predicted on different example in real analysis. The recurrent backpropagation is one of the best optimization techniques for minimizing the error and achieve the best optimal result. Since we have input unit, output unit, and eventually hidden unit; we could say that this is supervised optimization learning process. The optimization process to minimizing the error and get the activated network till that all weight of the network are going to reach equilibrium state). This process usually take more time because every output of network add-up with input again and train network (weight) to reach the equilibrium state (optimal solution).Reported results and graphical user interface (GUI) snapshot, showing algorithms are integrated well in software package (simulator).