Support vector machines and learning about time

The analysis of temporal data is an important issue of current research, because most real-world data either explicitly or implicitly contains some information about time. The key to successfully solving temporal learning tasks is to analyze the assumptions that can be made and prior knowledge one has about the temporal process of the learning problem and find a representation of the data and a learning algorithm that makes effective use of this knowledge. This paper will present a concise overview of the application Support Vector Machines to different temporal learning tasks and the corresponding temporal representations. --

Enhanced services for targeted information retrieval by event extraction and data mining

Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Question-answering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user's question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament. --

Structuring music collections by exploiting peers' processing

Music collections are structured in very different ways by different useres. There is not one general taxonomy, but individual, user-specific structures exist. Most users appreciate some support in structering their collection. A large variety of methods has been developed for textual collections. However, audio data are completely different. In this paper, we present a peer to peer scenario where a music collection is enhanced a set of audio data in a node of the user's taxonomy by retrieving (partial) taxonomies of peers. In order to classify audio data into a taxonomy features need to be extracted. Adopting feature extraction to a particular set of classes is effective but not efficient. Hence, we propose again to exploit what has allready been done. Wellsuited feature extraction for one classification task is transferred to similar tasks using a new distance measures. --

Enhanced Services for Targeted Information Retrieval by Event Extraction and Data Mining

Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Questionanswering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user’s question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament.

D-optimal plans in observational studies

This paper investigates the use of Design of Experiments in observational studies in order to select informative observations and features for classification. D-optimal plans are searched for in existing data and based on these plans the variables most relevant for classification are determined. The adapted models are then compared with respect to their predictive accuracy on an independent test sample. Eight different data sets are investigated by this method. --D-optimality,Genetic Algorithm,Prototypes,Feature Selection

Learning action oriented perceptual features for robot navigation

Machine learning can offer an increase in the flexibility and applicability of robotics at several levels of control. In this paper, we characterize two symbolic learning tasks in the field of robotics. We outline an approach for learning features from sensory data and for using these features to learn more complex ones. We illustrate our approach with first experiments in the field of navigation. The paper is written in English

Learning feature extraction for learning from audio data

Today, large collections of digital music plays are available. These audio data are time series which need to be indexed and classified for diverse applications. Indexing and classification differs from time series analysis, in that it generalises several series, whereas time series analysis handles just one series a time. The classification of audio data cannot use similarity measures defined on the raw data, e.g. using time warping, or generalise the shape of the series. The appropriate similarity or generalisation for audio data requires feature extraction before classification can successfully be applied to the transformed data. Methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires to tailor the feature set anew. Hence, we consider the construction of feature extraction methods from elementary operators itself a first learning step. We use a genetic programming approach. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences --