Elementary Derivative Tasks and Neural Net Multiscale Analysis of Tasks

Neural nets are known to be universal approximators. In particular, formal neurons implementing wavelets have been shown to build nets able to approximate any multidimensional task. Such very specialized formal neurons may be, however, difficult to obtain biologically and/or industrially. In this paper we relax the constraint of a strict ``Fourier analysis'' of tasks. Rather, we use a finite number of more realistic formal neurons implementing elementary tasks such as ``window'' or ``Mexican hat'' responses, with adjustable widths. This is shown to provide a reasonably efficient, practical and robust, multifrequency analysis. A training algorithm, optimizing the task with respect to the widths of the responses, reveals two distinct training modes. The first mode induces some of the formal neurons to become identical, hence promotes ``derivative tasks''. The other mode keeps the formal neurons distinct.Comment: latex neurondlt.tex, 7 files, 6 figures, 9 pages [SPhT-T01/064], submitted to Phys. Rev.

Prospective modelling of environmental dynamics. A methodological comparison applied to mountain land cover changes

During the last 10 years, scientists performed significant advances in modelling environmental dynamics. A wide range of new methodological approaches in geomatics - such as neural networks, multi-agent systems or fuzzy logics - was developed. Despite these progresses, the modelling softwares available have to be considered as experimental tools rather than as improved procedures able to work for environmental management or decision support. Particularly, the authors consider that a large number of publications suffer from lakes in the validation of the model results. This contribution describes three different modelling approaches applied to prospective land cover prediction. The first one, a combined geomatic method, uses Markov chains for temporal transition prediction while their spatial assignment is supervised manually by the construction of suitability maps. Compared to this directed method, the two others may be considered as semi automatic because both the polychotomous regression and the multilayer perceptron only need to be optimized during a training step - the algorithms detect themselves the spatial-temporal changes in land cover. The authors describe the three methodological approaches and their practical applications to two mountain studied areas: one in French Pyrenees, the second including a large part of Sierra Nevada, Spain. The article focuses on the comparison of results. The main result is that prediction scores are on the more high that land cover is persistent. They also underline that the geomatic model is complementary to the statistical ones which perform higher overall prediction rate but produce worse simulations when land cover changes are numerous

Spectral high resolution feature selection for retrieval of combustion temperature profiles

Proceeding of: 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 (Burgos, Spain, September 20-23, 2006)The use of high spectral resolution measurements to obtain a retrieval of certain physical properties related with the radiative transfer of energy leads a priori to a better accuracy. But this improvement in accuracy is not easy to achieve due to the great amount of data which makes difficult any treatment over it and it's redundancies. To solve this problem, a pick selection based on principal component analysis has been adopted in order to make the mandatory feature selection over the different channels. In this paper, the capability to retrieve the temperature profile in a combustion environment using neural networks jointly with this spectral high resolution feature selection method is studied.Publicad

A Neural Networks Committee for the Contextual Bandit Problem

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.Comment: 21st International Conference on Neural Information Processin

Infering Air Quality from Traffic Data using Transferable Neural Network Models

This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Consumer behaviour in the waiting area

Objective of the study: To determine consumer behaviour in the pharmacy waiting area. Method: The applied methods for data-collection were direct observations. Three Dutch community pharmacies were selected for the study. The topics in the observation list were based on available services at each waiting area (brochures, books, illuminated new trailer, children’s play area, etc.). Per patient each activity was registered, and at each pharmacy the behaviour was studied for 2 weeks. Results: Most patients only waited during the waiting time at the studied pharmacies. Few consumers obtained written information during their wait. Conclusion: The waiting area may have latent possibilities to expand the information function of the pharmacy and combine this with other activities that distract the consumer from the wait. Transdisciplinary research, combining knowledge from pharmacy practice research with consumer research, has been a useful approach to add information on queueing behaviour of consumers

Symmetry constrained machine learning

Symmetry, a central concept in understanding the laws of nature, has been used for centuries in physics, mathematics, and chemistry, to help make mathematical models tractable. Yet, despite its power, symmetry has not been used extensively in machine learning, until rather recently. In this article we show a general way to incorporate symmetries into machine learning models. We demonstrate this with a detailed analysis on a rather simple real world machine learning system - a neural network for classifying handwritten digits, lacking bias terms for every neuron. We demonstrate that ignoring symmetries can have dire over-fitting consequences, and that incorporating symmetry into the model reduces over-fitting, while at the same time reducing complexity, ultimately requiring less training data, and taking less time and resources to train

The Role of Distal Variables in Behavior Change: Effects of Adolescents\u27 Risk for Marijuana Use on Intention to Use Marijuana

This study uses an integrative model of behavioral prediction as an account of adolescents\u27 intention to use marijuana regularly. Adolescents\u27 risk for using marijuana regularly is examined to test the theoretical assumption that distal variables affect intention indirectly. Risk affects intention indirectly if low-risk and high-risk adolescents differ on the strength with which beliefs about marijuana are held, or if they differ on the relative importance of predictors of intention. A model test confirmed that the effect of risk on intention is primarily indirect. Adolescents at low and high risk particularly differed in beliefs concerning social costs and costs to self-esteem. Not surprisingly, at-risk adolescents took a far more positive stand toward using marijuana regularly than did low-risk adolescents. On a practical level, the integrative model proved to be an effective tool for predicting intention to use marijuana, identifying key variables for interventions, and discriminating between target populations in terms of determinants of marijuana use

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Probability of local bifurcation type from a fixed point: A random matrix perspective

Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure