Boosting interval based literals
WebCiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A supervised classification method for time series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals. Two types of predicates are used: i) relative … WebThe results are very competitive with the reported in previous works, and their comprehensibility is better than in other approaches with similar results, since the classifiers are formed by a weighted sequence of literals. A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple …
Boosting interval based literals
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http://journal.iberamia.org/public/ia-old/articles/286/article%20(1).pdf WebNov 1, 2000 · Some previous works like [2] [15] addressed this problem with the same data sets but using classifiers based on literals over temporal intervals (such as " the value on the series increases ...
WebAug 1, 2001 · It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals. Two types of … WebThe induced classifiers consist of a linear combination of literals, obtained by boosting base classifiers that contain only one literal. Nevertheless, these literals are specifically designed for the task at hand and they test properties of fragments of the time series on temporal intervals. The method had already been developed for fixed ...
WebA supervised classification method for time series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The … WebAug 1, 2005 · Normally, boosting [1] is used with well-known base classifiers, such as decision trees or neural networks. Hence, its main contribution is the capacity of …
WebMay 3, 2001 · Boosting interval based literals. Juan J. Rodríguez, Carlos J. Alonso, Henrik Boström; pp 245–262. A supervised classification method for time series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals.
WebThis predicate is introduced to test the results obtained with boosting without using interval based predicates. Two kinds of interval predicates are used: relative and region based. … have sth at one\u0027s fingertipsWebBoosting Interval Based Literals. 2000. [View Context]. Kagan Tumer and Joydeep Ghosh. Robust Combining of Disparate Classifiers through Order Statistics. CoRR, csLG/9905013. 1999. [View Context]. Chun-Nan Hsu … borth hallWebIt is based on boosting very simple classifiers: only one literal. The used predicates are based on temporal intervals. There are two types of predicates: i) relative predicates, … have sth attachedWebIt is based on boosting very simple classifiers: only one literal. The used predicates are based on temporal intervals. There are two types of predicates: i) relative predicates, … borth health centreWebKeywords: time series classification, interval based literals, boosting, machine learning . DOI: 10.3233/IDA-2001-5305 Citation: Intelligent Data Analysis, vol. 5, no. 3, pp. 245-262, 2001 Price: EUR 27.50. Add to cart. Select this result for bulk action Neural-morphological approach for pattern classification ... have sth. doingWebThe Boost CRC Library provides two implementations of CRC (cyclic redundancy code) computation objects and two implementations of CRC computation functions. The implementations are template-based. Author(s) Daryle Walker First Release 1.22.0 Categories Domain Specific Date Time. A set of date-time libraries based on generic … borth here and nowWebAug 1, 2005 · Our weak classifiers, interval-based literals, consider what happens in a given interval, e.g. what is the average value. These classifiers are very simple, but are … have sth available