Keyword cloud

Adaboost • API • C4.5 • cascade of classifiers • class imbalance • class list • classification speed • classifier • classifier approximation • classifier execution • classifier selection • color • confusion matrices • cross-validation • custom algorithms • customization • DB • decision tree • detectors • discriminants • error measurements • evaluation • feature selection • greedy search • image data • Image feature extraction • image view • interactive tools • KNN • LDA • leave-one-out • LIBSVM • marker • meta-data • multi-class • Multi-modal data • multi-scale • neural networks • nominal features • non-linear problems • operating point • operating points • optimization • outliers • output thresholding • output weighting • Parzen • PCA • PRTools • random forest • rejection • ROC • ROC analysis • sample meta-data • scatter plot • setting operation point • soft outputs • support vector machines • visual inspection • 

All articles

• kb34: Adjusting marker and color style of each class
• kb33: Tagging unsure decisions
• kb32: Speeding up classifier execution by joining pipeline steps
• kb31: Measuring classifier speed
• kb30: How to extract image features at multiple scales
• kb29: How to choose a classifier for a multi-modal problem
• kb28: Example on building a classifier on database records
• kb27: Upgrading to perClass 4
• kb26: Useful tips for confusion matrices
• kb25: Custom callback functions for sdscatter
• kb24: Example on building an image detector
• kb23: Example on image classification
• kb22: Note on decision tree performance and speed
• kb21: Feature selection in perClass
• kb20: PRSD Studio to perClass transition
• kb19: PRTools compatibility
• 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?