{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T04:12:08Z","timestamp":1777263128332,"version":"3.51.4"},"reference-count":53,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In machine learning, hyperparameter tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Several approaches have been widely adopted for hyperparameter tuning, which is typically a time consuming process. We propose an efficient technique to speed up the process of hyperparameter tuning with Grid Search. We applied this technique on text categorization using kNN algorithm with BM25 similarity, where three hyperparameters need to be tuned. Our experiments show that our proposed technique is at least an order of magnitude faster than conventional tuning.<\/jats:p>","DOI":"10.1515\/comp-2019-0011","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T09:03:45Z","timestamp":1565687025000},"page":"160-180","source":"Crossref","is-referenced-by-count":115,"title":["Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity"],"prefix":"10.1515","volume":"9","author":[{"given":"Raji","family":"Ghawi","sequence":"first","affiliation":[{"name":"Technical University of Munich , Germany Munich"}]},{"given":"J\u00fcrgen","family":"Pfeffer","sequence":"additional","affiliation":[{"name":"Technical University of Munich , Germany Munich"}]}],"member":"374","published-online":{"date-parts":[[2019,8,8]]},"reference":[{"key":"2022042707443478099_j_comp-2019-0011_ref_001_w2aab3b7c10b1b6b1ab1ab1Aa","unstructured":"[1] Claesen M., Moor B.D., Hyperparameter Search in Machine Learning, CoRR, abs\/1502.02127, 2015"},{"key":"2022042707443478099_j_comp-2019-0011_ref_002_w2aab3b7c10b1b6b1ab1ab2Aa","unstructured":"[2] Bergstra J., Bengio Y., Random Search for Hyper-parameter Optimization, Journal of Machine Learning Research, 13(1), 2012, 281\u2013305"},{"key":"2022042707443478099_j_comp-2019-0011_ref_003_w2aab3b7c10b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] Chapelle O., Vapnik V., Bousquet O., Mukherjee S., Choosing Multiple Parameters for Support Vector Machines, Machine Learning, 46(1-3), 2002, 131\u2013159, 10.1023\/A:101245032738710.1023\/A:1012450327387","DOI":"10.1023\/A:1012450327387"},{"key":"2022042707443478099_j_comp-2019-0011_ref_004_w2aab3b7c10b1b6b1ab1ab4Aa","unstructured":"[4] Do C.B., Foo C.S., Ng A.Y., Efficient Multiple Hyperparameter Learning for Log-linear Models, In Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS\u201907, Curran Associates Inc., USA, 2007, 377\u2013384"},{"key":"2022042707443478099_j_comp-2019-0011_ref_005_w2aab3b7c10b1b6b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] Wang Z., Hutter F., Zoghi M., Matheson D., De Freitas N., Bayesian Optimization in a Billion Dimensions via Random Embeddings, Journal of Artificial Intelligence Research, 55(1), 2016, 361\u201338710.1613\/jair.4806","DOI":"10.1613\/jair.4806"},{"key":"2022042707443478099_j_comp-2019-0011_ref_006_w2aab3b7c10b1b6b1ab1ab6Aa","unstructured":"[6] Bergstra J., Bardenet R., Bengio Y., K\u00e9gl B., Algorithms for Hyper-parameter Optimization, In Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS\u201911, Curran Associates Inc., USA, 2011, 2546\u20132554"},{"key":"2022042707443478099_j_comp-2019-0011_ref_007_w2aab3b7c10b1b6b1ab1ab7Aa","unstructured":"[7] Snoek J., Larochelle H., Adams R.P., Practical Bayesian Optimization of Machine Learning Algorithms, In Proceedings of the 25th International Conference on Neural Information Processing Systems - 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