With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However...

The contribution of this paper to the space transportation system field is to select promising Reusable Launch Vehicle (RLV) concepts by using a formal evaluation procedure. The vehicle system is divided into design features. Every design feature can have alternative characteristics. All combinations of design features and characteristics are compared pairwise with each other. The innovation an...

We propose an image segmentation algorithm based on local measures and fuzzy feature space analysis. We address the problem of assigning individual pixels to multiple classes based on computed pixel properties. This new segmentation algorithm extends an object/background segmentation approach introduced in 1992. The idea of analyzing trajectories in fuzzy feature space leads to the partitioning...

In our recent work we have introduced a framework for extracting features from solid of mechanical artifacts in polyhedral representation based on scale-space feature decomposition [1]. Our approach used recent developments in efficient hierarchical decomposition of metric data using its spectral properties. In that work, through spectral decomposition, we were able to reduce the problem of mat...

Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability of a clustering method applied to features extracted from the data. This approach is extremely versatile and e...

Temporal Feature Space generates features sequentially over consecutive time frames, thus producing a very large dimensional feature space cumulatively in contrast to the one which generates samples over time. Pattern Recognition applications for such temporal feature space therefore have to withstand the complexities involved with waiting for the arrival of new features over time and handling ...

In Machine Learning, the success and performance of learning often critically depends on the feature space provided. For instance, when learning a classifier f1, . . . , fn → c that maps features f1, . . . , fn to classes c, appropriate encodings of f1, . . . , fn are often as important as the choice of learning algorithm itself. This is particularly true for robot learning, where feature space...