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Cylinder Bands Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info.

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Juan J Rodríguez Diez and Carlos Alonso González and Henrik Boström. Learning First Order Logic Time Series Classifiers: Rules and Boosting. PKDD. 2000.

for classi#cation of time series are not easy to find [16]. For this reason we have used four artificial datasets and only one eal world" dataset. Cylinder Bell and Funnel (CBF). This is an artificial problem, introduced in [26]. The learning task is to distinguish between three classes: cylinder (c), bell


Juan J. Rodr##guez and Carlos J. Alonso. Applying Boosting to Similarity Literals for Time Series Classification. Department of Informatics University of Valladolid, Spain. 2000.

in the same way than the previous one, but 19 points are added at the end of each example, with mean 0 and variance 6 1. Again, we used the first 300 examples of each class of the corresponding dataset from the UCI ML Repository. Cylinder Bell and Funnel (CBF). This is an artificial problem, introduced by Saito [Saito, 1994]. The learning task is to distinguish between these three classes:


Juan J. Rodr##guez and Carlos J. Alonso and Henrik Bostrom. Boosting Interval Based Literals. 2000.

.0.048 0.214 4 0.704 0.084 0.781 0.676 .0.002 0.135 0.086 .0.039 0.147 0.192 Signi#c. 5 0.186 .0.005 .0.003 .0.002 .4e-05 .0.001 .0.001 .0.002 .3e-04 .0.002 Table 8: Results for the Shifted Wave data set Cylinder Bell Funnel -2 0 2 4 6 8 20 40 60 80 100 120 -2 0 2 4 6 8 20 40 60 80 100 120 -2 0 2 4 6 8 20 40 60 80 100 120 Figure 8: Examples of the CBF data set. Iter.: 10 20 30 40 50 60 70 80 90 100


Juan J. Rodr##guez and Carlos J. Alonso and Henrik Bostrom. Learning First Order Logic Time Series Classifiers: Rules and Boosting. Grupo de Sistemas Inteligentes, Departamento de Inform#atica Universidad de Valladolid, Spain.

for classification of time series are not easy to find [12]. For this reason we have used four artificial datasets and only one eal world" dataset: { Cylinder Bell and Funnel (CBF). This is an artificial problem [12], in which there are there are 3 classes: cylinder, bell and funnel. Figure 2.a shows two


Charles Campbell and Nello Cristianini. Simple Learning Algorithms for Training Support Vector Machines. Dept. of Engineering Mathematics.

problem of Gorman and Sejnowski [14] consists of 208 instances each with 60 attributes (excluding the labels) representing returns from a roughly cylindrical rock or a metal cylinder This dataset is equally divided into training and test sets. For the aspect-angle dependent dataset these authors trained a standard backpropagation neural network with 60 inputs and 2 output nodes. Experiments


Carlos J. Alonso Gonzalez and Juan J. Rodr and iguez Diez. Time Series Classification by Boosting Interval Based Literals. Grupo de Sistemas Inteligentes Departamento de Informatica Universidad de Valladolid.

for classification of time series are not easy to find [9]. For this reason we have used four artificial datasets and only one "real world" dataset: Cylinder Bell and Funnel (CBF). This is an artificial problem, introduced in [12]. The learning task is to distinguish between three classes: cylinder (c), bell


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