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
Machine Learning
IEEE Intelligent Systems
Towards Simple, Easy-to-Understand, yet Accurate Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Visual Methods for Examining SVM Classifiers
Visual Data Mining
Towards Effective Visual Data Mining with Cooperative Approaches
Visual Data Mining
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interactive optimization for steering machine classification
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cluster-Based sampling approaches to imbalanced data distributions
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Using multiple models to understand data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Visual exploration of classification models for various data types in risk assessment
Information Visualization - Special issue on Best Papers of Visual Analytics Science and Technology (VAST) 2010
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This paper discusses visual methods that can be used to understand and interpret the results of classification using support vector machines (SVM) on data with continuous real-valued variables. SVM induction algorithms build pattern classifiers by identifying a maximal margin separating hyperplane from training examples in high dimensional pattern spaces or spaces induced by suitable nonlinear kernel transformations over pattern spaces. SVM have been demonstrated to be quite effective in a number of practical pattern classification tasks. Since the separating hyperplane is defined in terms of more than two variables it is necessary to use visual techniques that can navigate the viewer through high-dimensional spaces. We demonstrate the use of projection-based tour methods to gain useful insights into SVM classifiers with linear kernels on 8-dimensional data.