Selection of Training Data

The molecules and infrared spectra selected for training have a profound influence on the radial distribution function derived from the CPG network and on the quality of 3D structure derivation. Training data are typically selected dynamically that is, each query spectrum selects its own set of training data by searching the most similar infrared spectra, or most similar input vector. Two similarity measures for infrared spectra are useful  

The Pearson correlation coefQcient between query spectrum and database spectrum may be used to describe the general shape of an infrared spectrum. 

It does not overestimate unspecific deviations, like strong band-intensity differences, or differences due to sample preparation errors or impurities. 

The root mean square (RMS) error between two spectra reacts more sensitively to global intensity differences and small changes, for instance, in the fingerprint region. 

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The selection of training data presented to the neural network influences whether or not the network learns a particular task. Some major considerations include the generalization/memorization issue, the partitioning of the training and prediction sets, the quality of data, the ratio of positive and negative examples, and the order of example presentation. 

Yet another problem, one more appropriately addressed by researchers in genome informatics, is the selection of training data. Obviously, training data should be... 

If the appropriate descriptors have been selected, a data set can be compiled for the training of the CPG neural network. A series of several hundred of H-NMR chemical shifts for protons of different molecular structures are required for such training. Typically, the available data set is divided into training and test (cross-validation) set that is, a part is used for training, whereas the rest of the data set is treated as unknown and used to derive the predictive quality of the method. Since the selection of training data determines the predictive quality, some key factors have to be accounted for ... 

Fig. 16 Selection of training data from satellite image...

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. 

Equations (24) and (25) are adequate for designing decision trees. The feature that minimizes the information content is selected as a node. This procedure is repeated for every leaf node until adequate classification is obtained. Techniques for preventing overfitting of training data, such as cross validation are then applied. 

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... 

As mentioned above, the test errors depend on the selection of training (calibration) and test data sets. We can get an idea about the distribution of the test errors by... 

The amount of data needed to validate a QSAR model is a matter of debate. The better the selection of test data (i.e., the greater the coverage of the applicability domain), the fewer data that are needed. Another consideration is whether some of the test data should fall outside the predefined domain of the QSAR, in order to explore further the limits of applicability. In the absence of other considerations, a working suggestion is that a minimal amount of 30% of the total number of compounds in the training set should be included in the test set. 

The main aspects for metamodel generation are (i) Generation of training data, through an experimental design strategy (ii) Independent variable selection (iii) Parameter estimation and (iv) Metamodel validation. 

Selection of the data set A total of 115 carboxylic and sulfonic acids were taken from the article published by Wronski (35). These acids are shown in Table 14.2. The data set has been divided into three sets a training, a prediction, and a test set consisting of 73, 23, and 19 molecules, respectively. The test set was randomly selected from the training set for controlling the construction of the ANFIS model. The prediction set was used for the evaluation of the generated models. 

With selected samples of training data to train the network model, and stop training when achieve the error ... 

Steps 5-7 are necessarily iterative and it should be expected that several modifications to both training data picking and/or selection of input data (attribute cubes) are required before a satisfactory result is achieved. In order to speed up the process, the workflow can be first run on a representative subvolume. After the training data have been picked, checked and parameters optimised, the neural network classification can then be run on the full volume of interest (Figure 3). 

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. 

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