It is acknowledged that overfitting can occur in feature selection using the wrapper method when there is a limited amount of training data available. It has also been shown that the severity of overfitting is related to the intensity of the search algorithm used during this process. In this paper we show that two stochastic search techniques (Simulated Annealing and Genetic Algorithms) that ca...

Automated techniques to optimise the retrieval of relevant cases in a CBR system are desirable as a way to reduce the expensive knowledge acquisition phase. This paper concentrates on feature selection methods that assist in indexing the case-base, and feature weighting methods that improve the similarity-based selection of relevant cases. Two main types of method are presented: filter methods ...

Continual advances in the manufacturing processes of integrated circuits provide designers the ability to create more complex and denser architectures and increased functionality on a single chip. The increased usage of embedded cores necessitates a core-based test strategy in which cores are also tested separately. The IEEE P1500 proposed standard for Embedded Core Test (SECT) is a standard un...

This paper describes the package maxLik for the statistical environment R. The package is essentially a unified wrapper interface to various optimization routines, offering easy access to likelihood-specific features like standard errors or information matrix equality (BHHH method). More advanced features of the optimization algorithms, such as forcing the value of a particular parameter to be ...

The web of data is a web vision in which data are represented into RDF and interlinked to each other. The fast growth of linked data motivates researchers to exploit this huge amount of well-represented data in linked data cloud. Moreover, finding the implicit answers is important to enable web agent to understand deeply web data. This paper proposes a SPARQL endpoint wrapper to enable original...

Within the taxonomy of feature extraction methods, recently the Wrapper approaches lost some popularity due to the associated computational burden, compared to Embedded or Filter methods. The dominating factor in terms of computational costs is the number of adaption cycles used to train the black box classifier or function approximator, e.g. a Multi Layer Perceptron. To keep a wrapper approach...

BACKGROUND In body surface potential mapping, increased spatial sampling is used to allow more accurate detection of a cardiac abnormality. Although diagnostically superior to more conventional electrocardiographic techniques, the perceived complexity of the Body Surface Potential Map (BSPM) acquisition process has prohibited its acceptance in clinical practice. For this reason there is an inte...

We present an unsupervised feature dimension reduction method for the classification of magnetic resonance spectra. The technique preserves spectral information, important for disease profiling. We propose to use this technique as a preprocessing step for computationally demanding wrapper-based feature subset selection. We show that the classification accuracy on an independent test set can be ...

The paper presents a method for testing a system-on-achip by using a compressed representation of the patterns on an external tester. The patterns for a certain core under test are decompressed by reusing scan chains of cores idle during that time. The method only requires a few additional gates in the wrapper, while the mission logic is untouched. Storage and bandwidth requirements for the ATE...

Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generates the examples changes over time. We present a method for detection of changes in the probability distribution of examples. The idea behind the drift detection method is to monito...