September 2014
Intermediate to advanced
384 pages
11h 1m
English
Content preview from Combining Pattern Classifiers: Methods and Algorithms, 2nd Edition
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INDEX
- AdaBoost
- reweighting and resampling
- AdaBoost.M1
- AdaBoost.M2
- face detection
- training error bound
- variants of
- Arcing
- Attribute
- Bagging
- nice
- out-of-bag data
- pasting small votes
- Bayes decision theory
- Bayesian learning
- Bias and variance
- Bonferroni-Dunn correction
- Bootstrap sampling
- Class
- labels
- soft
- uncertain
- prevalence
- separability
- labels
- Classes
- equiprobable
- linearly separable
- overlapping
- unbalanced
- Classification
- boundaries
- accuracy
- margin
- regions
- Classifier
- =inducer
- =learner
- base
- canonical model
- comparison
- complexity
- decision tree
- k-nearest neighbor (k-nn)
- prototype
- reference set
- largest prior
- linear discriminant (LDC)
- regularization of
- Naïve Bayes
- nearest mean (NMC)
- neural networks
- non-metric
- output
- abstract level
- correlation
- independent
- measurement level
- oracle
- output calibration
- performance of
- quadratic discriminant (QDC)
- selection
- support vector machine (SVM)
- unstable
- Classifier competence
- direct k-nn estimate
- distance-based k-nn estimate
- map
- potential functions
- pre-estimated regions
- Classifier selection
- cascade
- clustering and selection
- dynamic
- local class accuracy
- regions of competence
- Clustering
- hierarchical
- k-means
- non-hierarchical
- single linkage
- chain effect
- Combiner
- average
- Behavior Knowledge Space (BKS)
- competition jury
- decision templates
- equivalence of
- generalized mean
- level of optimism
- linear regression
- majority vote
- median
- minimum/maximum
- multinomial
- Naïve Bayes
- non-trainable
- optimality
- oracle
- plurality ...
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