One of the issues associated with pattern classification using databased machine learning systems is the “curse of dimensionality”. In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for ...

In this paper we propose a method to select an experimental design for estimating nonlinear simulation metamodels. Through a careful selection of design points, the method provides better fitting results than equally spaced point selection, with the same simulation effort. This method accounts for the input/output function of the simulation model, possibly a mathematical function nonlinear in t...

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This paper analyzes the effect of input profile selection on software testing using the concept of fault detectability profile. It shows that optimality of the input profile during testing depends on factors such as the planned testing effort and the fault detectability profile. To achieve ultra-reliable software, selecting test input uniformly among different input domains is preferred. On the...

Automatic speech recognition (ASR) is essential for a robot to communicate with people. One of the main problems with ASR for robots is that robots inevitably generate motor noises. The noise is captured with strong power by the robot’s microphones, because the noise sources are closer to the microphones than the target speech source. The signal-to-noise ratio of input speech becomes quite low ...

multilayer bach propagation neural networks have been considered by researchers. despite their outstanding success in managing contact between input and output, they have had several drawbacks. for example the time needed for the training of these neural networks is long, and some times not to be teachable. the reason for this long time of teaching is due to the selection unsuitable network par...

We present a fully automatic method for content selection evaluation in summarization that does not require the creation of human model summaries. Our work capitalizes on the assumption that the distribution of words in the input and an informative summary of that input should be similar to each other. Results on a large scale evaluation from the Text Analysis Conference show that input-summary...

The selection of input variables plays a crucial role when modelling time series. For nonlinear models there are not well developed techniques such as AIC and other criteria that work with linear models. In the case of Short Term Load Forecasting (STLF) generalization is greatly influenced by such selection. In this paper two approaches are compared using real data from a Spanish utility compan...

Input variable selection is a key preprocess step in any I/O modelling problem. Normally, better generalization performance is obtained when unneeded parameters coming from irrelevant or redundant variables are eliminated. Information theory provides a robust theoretical framework for performing input variable selection thanks to the concept of mutual information. Nevertheless, for continuous v...