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A novel 'Delay Vector Variance' (DVV) method for detecting the presence of determinism and nonlinearity in a time series is introduced. The method is based upon the examination of local predictability of a signal. Additionally, it spans the complete range of local linear models due to the standardisation to the distribution of pairwise distances between(More)
A new learning algorithm for kernel-based topographic map formation is introduced. The kernel parameters are adjusted individually so as to maximize the joint entropy of the kernel outputs. This is done by maximizing the differential entropies of the individual kernel outputs, given that the map's output redundancy, due to the kernel overlap, needs to be(More)
When using topographic maps for clustering purposes, which is now being considered in the data mining community, it is crucial that the maps are free of topological defects. Otherwise, a contiguous cluster could become split into separate clusters. We introduce a new algorithm for monitoring the degree of topology preservation of kernel-based maps during(More)
Support Vector Machines have demonstrated excellent results in pattern recognition tasks and 3D object recognition. In this contribution, we confirm some of the results in 3D object recognition and compare it to other object recognition systems. We use different pixel-level representations to perform the experiments , while we extend the setting to the more(More)
A novel method for determining the set of parameters for a phase space representation of a time series is proposed. Based upon the differential entropy, both the optimal embedding dimension , and time lag , are simultaneously determined. The choice of these parameters is closely related to the length of the optimal tap input delay line of an adaptive filter(More)
A hybrid filter/wrapper feature subset selection algorithm for regression is proposed. First, features are filtered by means of a relevance and redundancy filter using mutual information between regression and target variables. We introduce permutation tests to find statistically significant relevant and redundant features. Second, a wrapper searches for(More)
Most statistical signal nonlinearity analyses adopt the Monte-Carlo approach proposed by Theiler and co-workers, namely the 'surrogate data' method. A surrogate time series, or 'surrogate' for short, is generated as a realisation of the null hypothesis of linearity. A measure ('test statistic') is computed for the original time series and it is compared to(More)