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Artificial neural networks represent an alternative to traditional multivariate techniques, such as principal component and discriminant analysis, which rely on hypotheses regarding the normal distribution of the data and homoscedasticity. They also may be a powerful tool for multivariate modeling of systems that do not present linear correlation between variables, as well as to visualize high-dimensional data in bi- or trivariate structures. One special kind of neural network of interest in archaeometric studies is the Self-Organizing Map (SOM). SOMs can be distinguished from other neural networks for preserving the topological features of the original multivariate space. In this study, the self-organizing maps were applied to concentration data of chemical elements measured in archaeological ceramics from Central Amazon using instrumental neutron activation analysis (INAA). The main objective was testing the chemical patterns previously identified using cluster and principal component analysis, forming groups of ceramics according the multivariate chemical composition. It was verified by statistical tests that the chemical elemental data was not normally distributed and did not present homogeneity of covariance matrices for different groups, as requested by principal component analysis and other multivariate techniques. The maps obtained were consistent with the patterns identified by cluster and principal component analysis, forming two chemical groups of pottery shards for each archaeological site tested. Finally, it was verified the potential of SOMs for testing if failures in underlying hypotheses of traditional multivariate techniques might be critically influencing the results and subsequent archaeological interpretation of archaeometric data. [ABSTRACT FROM AUTHOR]

The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure–activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals. [ABSTRACT FROM AUTHOR]

Self-organizing maps (SOM) have become popular for tasks in data visualization, pattern classification or natural language processing and can be seen as one of the major contemporary concepts for artificial neural networks. The general idea is to approximate a high dimensional and previously unknown input distribution by a lower dimensional neural network structure so that the topology of the input space is mapped closely. Not only is the general topology retained but the relative densities of the input space are reflected in the final output. Kohonen maps also have the property of neighbor influence. That is, when a neuron decides to move, it pulls all of its neighbors in the same direction modified by an elasticity factor. We present a SOM that processes the whole input in parallel and organizes itself over time. The main reason for parallel input processing lies in the fact that knowledge can be used to recognize parts of patterns in the input space that have already been learned. Thus, networks can be developed that do not reorganize their structure from scratch every time a new set of input vectors is presented, but rather adjust their internal architecture in accordance with previous mappings. One basic application could be a modeling of the whole–part relationship through layered architectures. The presented neural network model implements growing parallel SOM structure for any input and any output dimension. The advantage of the proposed algorithm is in its property of processing the whole input space in one step. All nodes of the network compute their step simultaneously, and are, therefore, able to detect known patterns without reorganizing. The simulation results support the theoretical framework presented in the following sections. [ABSTRACT FROM AUTHOR]

The self-organizing Kohonen map is a reliable and efficient way to achieve vector quantization. Typical application of such algorithm is image compression. Moreover, Kohonen networks realize a mapping between an input and an output space that preserves topology. This feature can be used to build new compression schemes which allow to obtain better compression rate than with classical method as JPEG without reducing the image quality. Compared to JPEG, our lossy compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30 [C. Amerijckx, M. Verleysen, P. Thissen and J.-D. Legat (1998). Image compression by self-organized Kohonen map. IEEE Transactions on Neural Networks , 9 (3), 503-507.]. For lossless compression, this rate is about 2.7 for standard images. [ABSTRACT FROM AUTHOR]