This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formal...
We propose an efficient algorithm for mining frequent approximate sequential patterns under the Hamming distance model. Our algorithm gains its efficiency by adopting a "brea...
Many databases will not or can not be disclosed without strong guarantees that no sensitive information can be extracted. To address this concern several data perturbation techniq...
In this paper, we present Concept Chain Queries (CCQ), a special case of text mining in document collections focusing on detecting links between two topics across text documents. ...
Data mining techniques and machine learning methods are commonly used in several disciplines. It is possible that they could also provide a basis for quality assessment of softwar...
Abstract. Data Mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binaryvalued transactions, however the d...
Research in the field of knowledge discovery from temporal data recently focused on a new type of data: interval sequences. In contrast to event sequences interval sequences contai...
In this paper, we present an algorithm to identify types of places and objects from 2D and 3D laser range data obtained in indoor environments. Our approach is a combination of a c...
Kernel methods offer a flexible toolbox for pattern analysis and machine learning. A general class of kernel functions which incorporates known pattern invariances are invariant d...
In many classification applications, Support Vector Machines (SVMs) have proven to be highly performing and easy to handle classifiers with very good generalization abilities. Howe...