Data Mining: Foundations and Intelligent Paradigms: Volume - download pdf or read online

By Dawn E. Holmes, Lakhmi C Jain

ISBN-10: 364223240X

ISBN-13: 9783642232404

There are lots of necessary books to be had on information mining concept and functions. notwithstanding, in compiling a quantity titled “DATA MINING: Foundations and clever Paradigms: quantity 2: middle issues together with Statistical, Time-Series and Bayesian research” we want to introduce a number of the most recent advancements to a extensive viewers of either experts and non-specialists during this box.

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Read Online or Download Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects PDF

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Extra info for Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects

Example text

In the scatter plot, the diagonal line denotes the ideal method. Most of the SVM predictions follow this line, although the model tends to give higher errors for highly costly cars (top right of the plot). Only using domain knowledge it is possible to judge the quality of this predictive performance (although it should be stressed that better results can be achieved for this dataset, as in this example only 9 inputs were used). g used by car auction sites). Regarding the extraction of knowledge, the sensitivity analysis procedure revealed the curb weight as the most relevant factor.

Learning-based rule-extraction from support vector machines. In: 14th International Conference on Computer Theory and Applications ICCTA, Citeseer, vol. 2004 (2004) 3. : Pattern recognition and machine learning. Springer, New York (2006) 4. : Data mining and the impact of missing data. Industrial Management & Data Systems 103(8), 611–621 (2003) 5. : A Practical Guide to Support Vector Classification. Technical report, National Taiwan University (2003) 6. 0: Step-by-step data mining guide. CRISP-DM consortium (2000) 7.

As an example of explanatory knowledge, the left of Fig. 5. The plot shows that the most important input is alcohol, followed by the volatile acidity, pH and sulphates. Table 2. 04 Fig. 4. ROC curves for the white wine quality task (left) and SVM input importances (right) 21 0 0 10 10 20 20 30 30 40 40 Data Mining with Multilayer Perceptrons and Support Vector Machines 100 150 200 250 100 150 200 250 Fig. 5. 2 Regression Example The automobile dataset goal is to predict car prices using 25 continuous and categorical attributes.

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Data Mining: Foundations and Intelligent Paradigms: Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects by Dawn E. Holmes, Lakhmi C Jain

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