Training data sets

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 ... 

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. 

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. 

Figure 10.1-3. Two regioisomeric products of the training data set and their corresponding physicochemical effects used as coding vectors bo bond order difference in tr-electro-...

Figure 11.10. Single catalyst model predictions versus experimental initial NO storage/reduction results for catalyst 0.5Pt/7.5Ba/2.5Fe 4 for a) Training data set b) Validation data set for batch 2.

Let us consider now a training data set of n objects in the r-dimensional space, i.e., the vectors (points)Xi,. In the two-group case, we have the information... 

For LDA (Section 5.2.1) we select a training data set randomly (2/3 of the objects) and use the derived classification mle to predict the group membership of the... 

Prepared a training data set of chronic toxicity measurements to statistically establish the relationship between chronic toxicity and PAH descriptors of interest. 

Table 13. Summary of average values of the regression equation constants for the training data set (11 PAHs) on different solid phases (Note base 10 logs)... 

For the training data set (Table 11), few parameters of the 22 physical-chemical properties of the PAHs showed high significance vs log Koc. These are the log Kow, molecular weight, and molecular connectivity indices (3 Xp, 6X , 2XV). Table 14... 

The validation data set constitutes 42 PAHs (Table 11) comprising both unsubstituted and substituted compounds with a wide range of physical and chemical properties. Predictive models developed for PAH compounds in the training data set (Fig. 15) were used to predict values of sorption coefficients. All predicted and observed values were regressed, and recorded significant R2 values as shown in Figs. 16 and 17, while the difference between such values are presented in Table 11. 

Two generally different scenarios can be found for applications of machine learning technology so-called supervised and unsupervised learning. The difference is the presence or absence of observation of the desired output on a training data set. 

A series of Aroclors and known Aroclor mixtures were analyzed by these techniques to provide a training data set for SIMCA-3B. These standards Included replicate analyses, a 1 1 (w/w) mixture of each Aroclor in combination with one other Aroclor, and a 1 1 1 1 mixture of each Aroclor (Table I). 

Table I. Aroclors Samples Composing the Training Data Set.

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

Note that before scaling the training data set, one has to be aware of the absorbances that might occur on the validation set (in general, on the unknowns). This is so because the [0. . . 1] and [—1. .. -I-1] ranges should bracket not only the absorbances of the calibration samples but also those that might occur on the validation set and future unknowns. 

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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