Network training data

Cigizoglu, H.K. and O. Kisi Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data. Nordic Hydrol. 36 (2005) (inpress). 

The three introduced network structures were trained with the training data set and tested with the test dataset. The backpropagation network reaches its best classification result after 70000 training iterations ... 

The Fuzzy-ARTMAP network reaehes the best learning rate of the training data set. This is recognised perfectly with 100% correctness. The exact results are presented in the next table ... 

Neural networks model the functionality of the brain. They learn from examples, whereby the weights of the neurons are adapted on the basis of training data. 

Model building consists of three steps training, evaluation, and testing. In the ideal case the whole training data set is divided into three portions, the training, the evaluation set, and the test set. A wide variety of statistical or neural network... 

Breindl et. al. published a model based on semi-empirical quantum mechanical descriptors and back-propagation neural networks [14]. The training data set consisted of 1085 compounds, and 36 descriptors were derived from AMI and PM3 calculations describing electronic and spatial effects. The best results with a standard deviation of 0.41 were obtained with the AMl-based descriptors and a net architecture 16-25-1, corresponding to 451 adjustable parameters and a ratio of 2.17 to the number of input data. For a test data set a standard deviation of 0.53 was reported, which is quite close to the training model. 

When the structure is submitted its 3D coordinates arc calculated and the structure is shown at the left-hand side in the form of a 2D structure as well as a rotatable 3D structure (see Figure 10.2-11). The simulation can then be started the input structure is coded, the training data are selected, and the network training is launched. After approximately 30 seconds the simulation result is given as shown in Figure 10,2-11. 

This reaction data set of 626 reactions was used as a training data set to produce a knowledge base. Before this data set is used as input to a neural Kohonen network, each reaction must be coded in the form of a vector characterizing the reaction event. Six physicochemical effects were calculated for each of five bonds at the reaction center of the starting materials by the PETRA (see Section 7.1.4) program system. As shown in Figure 10,3-3 with an example, the physicochemical effects of the two regioisomeric products arc different. 

The abihty to generalize on given data is one of the most important performance charac teristics. With appropriate selection of training examples, an optimal network architec ture, and appropriate training, the network can map a relationship between input and output that is complete but bounded by the coverage of the training data. 

Providing input/output data is available, a neural network may be used to model the dynamies of an unknown plant. There is no eonstraint as to whether the plant is linear or nonlinear, providing that the training data eovers the whole envelope of plant operation. 

Fig. 10.26 Training and trained data fora neural network model of a Ship s Hull.

In a attempt to compensate for poor long-term reproducibility in a longterm identification study, Chun et al.128 applied ANNs to PyMS spectra collected from strains of Streptomyces six times over a 20-month period. Direct comparison of the six data sets, by the conventional approach of HCA, was unsuccessful for strain identification, but a neural network trained on spectra from each of the first three data sets was able to identify isolates in those three datasets and in the three subsequent datasets. 

Huang and Tang49 trained a neural network with data relating to several qualities of polymer yarn and ten process parameters. They then combined this ANN with a genetic algorithm to find parameter values that optimize quality. Because the relationships between processing conditions and polymer properties are poorly understood, this combination of AI techniques is a potentially productive way to proceed. 

For fitting a neural network, it is often recommended to optimize the values of A via C V. An important issue for the number of parameters is the choice of the number of hidden units, i.e., the number of variables that are used in the hidden layer (see Section 4.8.3). Typically, 5-100 hidden units are used, with the number increasing with the number of training data variables. We will demonstrate in a simple example how the results change for different numbers of hidden units and different values of A. 

FIGURE 5.18 Classification with neural network for two groups of two-dimensional data. The training data are shown with the symbol corresponding to the group membership. Any new data point would be classified according to the presented decision boundaries. The number of hidden units and the weight decay were varied. 

N.A. Woody and S.D. Brown, Hybrid Bayesian networks making the hybrid Bayesian classifier robust to missing training data, J. Chemom, 17, 266-273 (2003). 

Network predicted Property Training data set Testing data set ... 

A neural network is a program that processes data like (a part of) the nervous system. Neural networks are especially useful for classification problems and for function approximation problems which are tolerant of some imprecision, which have lots of training data available, but to which hard and fast rules (such as laws of nature) cannot easily be applied. 

Woody, N.A. and Brown, S.D., Hybrid Bayesian Networks Making the Hybrid Bayesian Classifier Robust to Missing Training Data /. Chemom. 2003, 17, 266-273. 

The rest of the paper is organized as follows. The Section 2 describes attack classification and training data set. In the Section 3 the intrusion detection system is described, based on neural network approach. Section 4 presents the nonlinear PCA neural network and multilayer perceptron for identification and classification of computer network attack. In Section 5 the results of experiments are presented. Conclusion is given in Section 6. 

To assess the effectiveness of the proposed intrusion detection approach, the experiments were conducted on the KDD Cup network intrusion detection data set [14]. We have used training data sets for anomaly detection made up of 400-700 randomly selected normal samples for each service. Training data sets for identification of attack made up of normal samples and attacks (Table 4) for each service. 

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