Keyword cloud

Adaboost  cascade of classifiers  class imbalance  classifier approximation  classifier execution  confusion matrices  cross-validation  custom algorithms  detectors  discriminants  error measurements  evaluation  image data  interactive tools  KNN  leave-one-out  LIBSVM  multi-class  neural networks  operating point  operating points  optimization  output thresholding  output weighting  Parzen  PRTools compatibility  rejection  ROC analysis  sample meta-data  setting operation point  soft outputs  support vector machines  

All articles

• kb18: How to protect a trained discriminant against outliers?
• kb17: How to optimize three-class classifier in imbalanced problems
• kb16: Visualize the effect of a change of parameters in a trained classifier
• kb15: How to speed up classifiers using the neural network approximation?
• kb14: How to train a two-stage algorithm?
• kb13: How to find samples with a specific type of error in a confusion matrix?
• kb12: Detector classifier cascade with ROC analysis
• kb11: Hierarchical classifier: How to build detector-classifier cascade?
• kb10: A step by step construction of a detector
• kb9: How to build a detector from a custom region in an image?
• kb8: How to build a detector in a single line of code?
• kb7: How to convert LIBSVM Support Vector machine into a pipeline?
• kb6: Can ROC analysis be performed for multi-class problems?
• kb5: ROC analysis on two-class problems: choosing an operating point
• kb4: How to cross-validate over objects?
• kb3: Perform leave-one-out evaluation
• kb2: How to perform cross-validation with replicas
• kb1: How to make decisions at a default operating point?