Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Digital Coding of Waveforms: Principles and Applications to Speech and Video
Digital Coding of Waveforms: Principles and Applications to Speech and Video
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Efficient Index Structures for String Databases
Proceedings of the 27th International Conference on Very Large Data Bases
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Manipulation, analysis and retrieval systems for audio signals
Manipulation, analysis and retrieval systems for audio signals
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
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In this paper we introduce a framework for automatic feature extraction from very large series. The extracted features build a new representation which is better suitable for a given learning task. The development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew. Therefore, the simple building blocks defined in our framework can be combined to complex feature extraction methods. We employ a genetic programming approach guided by the performance of the learning classifier using the new representation. Our approach to evolve representations from series data requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. 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 in the domain of music data classification: classification of genres and classification according to user preferences.