The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
The nature of statistical learning theory
The nature of statistical learning theory
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing the simple Baysian classifier
Information visualization in data mining and knowledge discovery
Gaining insights into support vector machine pattern classifiers using projection-based tour methods
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Information Visualization in Data Mining and Knowledge Discovery
Information Visualization in Data Mining and Knowledge Discovery
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Report on the SIGKDD-2002 panel the perfect data mining tool: interactive or automated?
ACM SIGKDD Explorations Newsletter
The Automated Multidimensional Detective
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
GGobi: evolving from XGobi into an extensible framework for interactive data visualization
Computational Statistics & Data Analysis - Data visualization
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
SVM and Graphical Algorithms: A Cooperative Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Using nonlinear dimensionality reduction to visualize classifiers
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Support vector machines (SVM) offer a theoretically wellfounded approach to automated learning of pattern classifiers. They have been proven to give highly accurate results in complex classification problems, for example, gene expression analysis. The SVM algorithm is also quite intuitive with a few inputs to vary in the fitting process and several outputs that are interesting to study. For many data mining tasks (e.g., cancer prediction) finding classifiers with good predictive accuracy is important, but understanding the classifier is equally important. By studying the classifier outputs we may be able to produce a simpler classifier, learn which variables are the important discriminators between classes, and find the samples that are problematic to the classification. Visual methods for exploratory data analysis can help us to study the outputs and complement automated classification algorithms in data mining. We present the use of tour-based methods to plot aspects of the SVM classifier. This approach provides insights about the cluster structure in the data, the nature of boundaries between clusters, and problematic outliers. Furthermore, tours can be used to assess the variable importance. We show how visual methods can be used as a complement to crossvalidation methods in order to find good SVM input parameters for a particular data set.