Search results for "Machine learning"
showing 10 items of 1464 documents
Distance Functions, Clustering Algorithms and Microarray Data Analysis
2010
Distance functions are a fundamental ingredient of classification and clustering procedures, and this holds true also in the particular case of microarray data. In the general data mining and classification literature, functions such as Euclidean distance or Pearson correlation have gained their status of de facto standards thanks to a considerable amount of experimental validation. For microarray data, the issue of which distance function works best has been investigated, but no final conclusion has been reached. The aim of this extended abstract is to shed further light on that issue. Indeed, we present an experimental study, involving several distances, assessing (a) their intrinsic sepa…
Regularized Regression Incorporating Network Information: Simultaneous Estimation of Covariate Coefficients and Connection Signs
2014
We develop an algorithm that incorporates network information into regression settings. It simultaneously estimates the covariate coefficients and the signs of the network connections (i.e. whether the connections are of an activating or of a repressing type). For the coefficient estimation steps an additional penalty is set on top of the lasso penalty, similarly to Li and Li (2008). We develop a fast implementation for the new method based on coordinate descent. Furthermore, we show how the new methods can be applied to time-to-event data. The new method yields good results in simulation studies concerning sensitivity and specificity of non-zero covariate coefficients, estimation of networ…
Bayesian versus data driven model selection for microarray data
2014
Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. A…
Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality
2015
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…
Active spike transmission in the neuron model with a winding threshold manifold
2012
International audience; We analyze spiking responses of excitable neuron model with a winding threshold manifold on a pulse stimulation. The model is stimulated with external pulse stimuli and can generate nonlinear integrate-and-fire and resonant responses typical for excitable neuronal cells (all-or-none). In addition we show that for certain parameter range there is a possibility to trigger a spiking sequence with a finite number of spikes (a spiking message) in the response on a short stimulus pulse. So active transformation of N incoming pulses to M (with M>N) outgoing spikes is possible. At the level of single neuron computations such property can provide an active "spike source" comp…
Contextual neural-network based spectrum prediction for cognitive radio
2015
Cognitive radio is the technique of effective electromagnetic spectrum usage important for future wireless communication including 5G networks. Neural networks are nature-inspired computational models used to solve cognitive radio prediction problems. This paper presents the use of contextual Sigma-if neural network in prediction of channel states for cognitive radio. Our results indicate that Sigma-if neural network confirms better predictions than Multilayer Perceptron (MLP) network and decreases sensing time for the benefit of the increase of the effectiveness of e-m spectrum usage.
Incremental Generalized Discriminative Common Vectors for Image Classification.
2015
Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without th…
On the Online Classification of Data Streams Using Weak Estimators
2016
In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most impo…
Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles
2016
Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The train…
Towards more Valid Assessment of Learning from Animations
2020
Animated explanations have become an ubiquitous feature of modern educational practice. They provide a distinctive, non-verbal means of presenting information that is particularly appropriate for dynamic subject matter. However, the prevailing approaches used to assess learning from educational animations are almost exclusively verbal. There is thus a clear disconnect between the form of representation students encounter during their learning activity and the very different form of representation used to assess the resulting learning outcomes. This fundamental inconsistency undermines the validity of current assessment approaches and signals the need for a fresh look at how learning from an…