Lazy learning in radial basis neural networks: A way of achieving more accurate models

Radial Basis Neural Networks have been successfully used in a large number of applications having in its rapid convergence time one of its most important advantages. However, the level of generalization is usually poor and very dependent on the quality of the training data because some of the training patterns can be redundant or irrelevant. In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be approximated. This training method follows a lazy learning strategy, in the sense that it builds approximations centered around the novel sample. The proposed method has been applied to three different domains an artificial regression problem and two time series prediction problems. Results have been compared to standard training method using the complete training data set and the new method shows better generalization abilities.Publicad

Evolving generalized euclidean distances for training RBNN

In Radial Basis Neural Networks (RBNN), the activation of each neuron depends on the Euclidean distance between a pattern and the neuron center. Such a symmetrical activation assumes that all attributes are equally relevant, which might not be true. Non-symmetrical distances like Mahalanobis can be used. However, this distance is computed directly from the data covariance matrix and therefore the accuracy of the learning algorithm is not taken into account. In this paper, we propose to use a Genetic Algorithm to search for a generalized Euclidean distance matrix, that minimizes the error produced by a RBNN.Publicad

Evolving Generalized Euclidean Distances for Training RBNN

In Radial Basis Neural Networks (RBNN), the activation of each neuron depends on the Euclidean distance between a pattern and the neuron center. Such a symmetrical activation assumes that all attributes are equally relevant, which might not be true. Non-symmetrical distances like Mahalanobis can be used. However, this distance is computed directly from the data covariance matrix and therefore the accuracy of the learning algorithm is not taken into account. In this paper, we propose to use a Genetic Algorithm to search for a generalized Euclidean distance matrix, that minimizes the error produced by a RBNN

Improving the Generalization Ability of RBNN Using a Selective Strategy Based on the Gaussian Kernel Function

Radial Basis Neural Networks have been successfully used in many applications due, mainly, to their fast convergence properties. However, the level of generalization is heavily dependent on the quality of the training data. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. In this paper, a learning method is presented, that automatically selects the training patterns more appropriate to the new test sample. The method follows a selective learning strategy, in the sense that it builds approximations centered around the novel sample. This training method uses a Gaussian kernel function in order to decide the relevance of each training pattern depending on its similarity to the novel sample. The proposed method has been applied to three different domains: an artificial approximation problem and two time series prediction problems. Results have been compared to standard training method using the complete training data set and the new method shows better generalization abilities

Data-aided Underwater Acoustic Ray Propagation Modeling

Acoustic propagation models are widely used in numerous oceanic and other underwater applications. Most conventional models are approximate solutions of the acoustic wave equation, and require accurate environmental knowledge to be available beforehand. Environmental parameters may not always be easily or accurately measurable. While data-driven techniques might allow us to model acoustic propagation without the need for extensive prior environmental knowledge, such techniques tend to be data-hungry and often infeasible in oceanic applications where data collection is difficult and expensive. We propose a data-aided ray physics based high frequency acoustic propagation modeling approach that enables us to train models with only a small amount of data. The proposed framework is not only data-efficient, but also offers flexibility to incorporate varying degrees of environmental knowledge, and generalizes well to permit extrapolation beyond the area where data was collected. We demonstrate the feasibility and applicability of our method through four numerical case studies, and one controlled experiment. We also benchmark our method's performance against classical data-driven techniques.Comment: Accepted version in IEEE Journal of Oceanic Engineerin

Artificial Intelligence in Materials Modeling and Design

In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented

Predicting IPO underpricing with genetic algorithms

This paper introduces a rule system to predict first-day returns of initial public offerings based on the structure of the offerings. The solution is based on a genetic algorithm using a Michigan approach. The performance of the system is assessed comparing it to a set of widely used machine learning algorithms. The results suggest that this approach offers significant advantages on two fronts: predictive performance and robustness to outlier patterns. The importance of the latter should be emphasized as the results in this domain are very sensitive to their presence.We acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR) and Comunidad de Madrid (CCG10-UC3M/TIC-5029)

Nearest neighbour imputation and variance estimation methods.

In large-scale surveys, non-response is a common phenomenon. This non-response can be of two types; unit and item non-response. In this thesis we deal with item non-response as other responses from the survey unit can be used for adjustment. Usually non-response adjustment is carried out in one of three ways; weighting, imputation and no adjustments. Imputation is the most commonly used adjustment method, either as single imputation or multiple imputations. In this thesis we study single imputation, in particular nearest neighbour methods, and we have developed a new method. Our method is based on dissimilarity measures and is nonparametric and handles categorical and continuous covariates without requiring any transformations. One drawback with this method was that it is relatively computer intensive, so we investigated data reduction methods. For data reduction we developed a new method that uses propensity scores. Propensity score is used as it has properties that suggest that it would make a good method for matching the respondents and non-respondents. We also looked at subset selection of the covariates using graphical modelling and principal component analysis. We found that the data reduction methods gave as good a result as when using all variables and there was considerable reduction in computation time especially with the propensity score method. As the imputed values are not true values, estimating the variance of the parameter of interest using standard methods would underestimate the variance if no allowance is made for the extra uncertainty due to imputed data being used. We examined various existing methods of variance estimation, particularly the bootstrap method, because both nearest neighbour imputation and bootstrap are non parametric. Also bootstrap is a unified method for estimating smooth as well as non-smooth parameters. Shao and Sitter (1996) proposed a bootstrap method, but for some extreme situations this method has problems. We have modified the bootstrap method of Shao and Sitter to overcome this problem and simulations indicate that both methods give good results. The conclusions from the study are that our new method of multivariate nearest neighbour is at least as good as regression based nearest neighbour and is often better. For large data sets, data reduction may be desirable and we recommend our propensity score method as it was observed to be the fastest among the subset selection methods as well as have some other advantages over the others. Imputing using any of the subsets methods we looked at appear to have similar results to imputing using all covariates. To compute the variance of the imputed data, we recommend the method proposed by Shao and Sitter or our modification of Shao and Sitter's method

Artificial Intelligence-Based Drug Design and Discovery

The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practical applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field