{ "data_id": "300", "name": "isolet", "exact_name": "isolet", "version": 1, "version_label": null, "description": "**Author**: Ron Cole and Mark Fanty (cole@cse.ogi.edu, fanty@cse.ogi.edu) \r\n**Donor**: Tom Dietterich (tgd@cs.orst.edu) \r\n**Source**: [UCI](https:\/\/archive.ics.uci.edu\/ml\/datasets\/ISOLET) \r\n**Please cite**: UCI \r\n\r\n### Description\r\n\r\nISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjects spoke the name of each letter of the alphabet twice. Hence, there are 52 training examples from each speaker. The speakers are grouped into sets of 30 speakers each, 4 groups can serve as training set, the last group as the test set. A total of 3 examples are missing, the authors dropped them due to difficulties in recording. \r\n\r\nThis is a good domain for a noisy, perceptual task. It is also a very good domain for testing the scaling abilities of algorithms. For example, C4.5 on this domain is slower than backpropagation!\r\n\r\n### Source\r\n\r\n* Creators: \r\nRon Cole and Mark Fanty \r\nDepartment of Computer Science and Engineering, \r\nOregon Graduate Institute, Beaverton, OR 97006. \r\ncole '@' cse.ogi.edu, fanty '@' cse.ogi.edu \r\n\r\n* Donor: \r\nTom Dietterich \r\nDepartment of Computer Science \r\nOregon State University, Corvallis, OR 97331 \r\ntgd '@' cs.orst.edu\r\n\r\n### Attributes Information\r\n \r\nAll attributes are continuous, real-valued attributes scaled into the range -1.0 to 1.0. The features are described in the paper by Cole and Fanty cited below. \r\nThe features include spectral coefficients; contour features, sonorant features, pre-sonorant features, and post-sonorant features. The exact order of appearance of the features is not known.\r\n\r\n### Relevant papers\r\n\r\nFanty, M., Cole, R. (1991). Spoken letter recognition. \r\nIn Lippman, R. P., Moody, J., and Touretzky, D. S. (Eds). Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann.\r\n\r\nDietterich, T. G., Bakiri, G. (1991) Error-correcting output codes: A general method for improving multiclass inductive learning programs. \r\nProceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Anaheim, CA: AAAI Press.\r\n\r\nDietterich, T. G., Bakiri, G. (1994) Solving Multiclass Learning Problems via Error-Correcting Output Codes.\r\n\r\n", "format": "ARFF", "uploader": "Tobias Kuehn", "uploader_id": 94, "visibility": "public", "creator": null, "contributor": null, "date": "2014-08-20 20:59:05", "update_comment": "all numeric variables declared as factors before", "last_update": "2014-09-25 23:26:09", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/52405\/phpB0xrNj", "default_target_attribute": "class", "row_id_attribute": null, "ignore_attribute": null, "runs": 43283, "suggest": { "input": [ "isolet", "### Description ISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjects spoke the name of each letter of the alphabet twice. Hence, there are 52 training examples from each speaker. The speakers are grouped into sets of 30 speakers each, 4 groups can serve as training set, the last group as the test set. A total of 3 examples are missing, the authors dropped them due to difficulties in recording. This is a good domain for a noisy, perceptual task. 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