Titlebook: Machine Learning and Data Mining in Pattern Recognition; 9th International Co Petra Perner Conference proceedings 2013 Springer-Verlag Berl

Filibuster

Analytic Feature Selection for Support Vector Machines,esults on high dimensional text data sets, with features that can be organized into a handful of feature types; for example, unigrams, bigrams or semantic structural features. We believe this algorithm is a novel and effective approach to solving the feature selection problem for linear SVMs.

認(rèn)識

Evaluation of Hyperspectral Image Classification Using Random Forest and Fukunaga-Koontz Transform,perimental results on AVIRIS hyperspectral image dataset that contains various types of field crops. In our experimental results, we have obtained overall classification accuracy around 84 percent for the classification of 16 types of field crops.

范例

The Gapped Spectrum Kernel for Support Vector Machines,lt, we obtain an algorithm to compute the wildcard kernel that is provably faster than the state-of-the-art method. The recently introduced .-suffix array data structure plays an important role here. Another result is a better trade-off between the speed and accuracy of classification, which we demonstrate by protein classification experiment.

Ataxia

Dynamic-Radius Species-Conserving Genetic Algorithm for the Financial Forecasting of Dow Jones Inde of the domain. This research applies the DSGA algorithm to training data which produces a set of rules. The rules are applied to a set of testing data to obtain results. The DSGA algorithm did very well in predicting stock movement.

Goblet-Cells

嚴(yán)厲批評

主動(dòng)

SOM++: Integration of Self-Organizing Map and K-Means++ Algorithms,lustering and data mining in terms of runtime, the rate of unstable data points and internal error. This paper also presents the comparison of our algorithm with simple SOM and K-Means + SOM by using a real world data. The results show that SOM++ has a good performance in stability and significantly outperforms three other methods training time.

爭吵

0302-9743 ical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.978-3-642-39711-0978-3-642-39712-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

Hangar

Typhoon Damage Scale Forecasting with Self-Organizing Maps Trained by Selective Presentation Learnictability of large scale damage by SOM. Learning data corresponding to middle and large scale damage are presented more often. Average accuracy for actually large scale damage data was increased by about 9%. The accuracy for actually large scale of numbers of fatalities and houses under water was increased by 25% and 20%, respectively.

類人猿

3D Geovisualisation Techniques Applied in Spatial Data Mining, afterwards, the efficiency of these techniques is demonstrated with the use of a real database. The application has shown to be very interesting in interpreting obtained results, such as patterns that occurred in a same locality and to provide support for activities which could be done as from the visualisation of results.