The nature of statistical learning theory
The nature of statistical learning theory
Structural Modelling with Sparse Kernels
Machine Learning
Kernel conditional random fields: representation and clique selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exponential families for conditional random fields
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Nomograms for visualization of naive Bayesian classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Artificial Intelligence in Medicine
Towards a Model Independent Method for Explaining Classification for Individual Instances
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Explaining instance classifications with interactions of subsets of feature values
Data & Knowledge Engineering
Proceedings of the 18th ACM conference on Information and knowledge management
A nomogram construction method using genetic algorithm and naïve Bayesian technique
MACMESE'09 Proceedings of the 11th WSEAS international conference on Mathematical and computational methods in science and engineering
An Efficient Explanation of Individual Classifications using Game Theory
The Journal of Machine Learning Research
ACM SIGKDD Explorations Newsletter
A general method for visualizing and explaining black-box regression models
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Nomogram visualization for ranking support vector machine
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Acquiring an ontology from the text a legal case study
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Quality of classification explanations with PRBF
Neurocomputing
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
Intelligent Data Analysis
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We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphically encode the complete model on a single page. The dimensionality of the visualization does not depend on the number of attributes, but merely on the properties of the kernel. To represent the effect of each predictive feature on the log odds ratio scale as required for the nomograms, we employ logistic regression to convert the distance from the separating hyperplane into a probability. Case studies on selected data sets show that for a technique thought to be a black-box, nomograms can clearly expose its internal structure. By providing an easy-to-interpret visualization the analysts can gain insight and study the effects of predictive factors.