Index

a

  • Acceptance boundary
  • Adaboost
  • Algorithm
    • backward
    • condensation
    • forward
    • forward–backward
    • Viterbi
  • Allele
  • Autoregressive, moving average models

b

  • Batch processing
  • Bayes estimation
  • Bayes' theorem
  • Bayesian classification
  • Bhattacharyya upper bound
  • Bias
  • Binary classification
  • Binary measurements
  • Boosting
  • Branch-and-bound

c

  • Chernoff bound
  • Chi-square test
  • Chromosome
  • Classifier
    • Bayes
    • Euclidean distance
    • least squared error
    • linear
    • linear discriminant function
    • Mahalanobis distance
    • maximum a posteriori (MAP)
    • minimum distance
    • minimum error rate
    • nearest neighbour
    • perceptron
    • quadratic
    • support vector
  • Clustering
    • average-link
    • characteristics
    • complete-link
    • hierarchical
    • K-----means
    • single-link
  • Completely
    • controllable
    • observable
  • Computational complexity
  • Computational issues
  • Condensation algorithm (conditional density optimization)
  • Condensing
  • Confusion matrix
  • Consistency checks
  • Continuous state
  • Control law
  • Control vector
  • Controllability matrix
  • Controller
  • Convolutional Neural Networks (CNNs)
  • Cost
    • absolute value
    • function
    • matrix
    • quadratic
    • uniform
  • Covariance
  • Covariance model (CVM) based estimator
  • Covariance models
  • Cross-validation
  • Crossover
  • Curve
    • calibration
    • fitting

d

  • Datafiles
  • Datasets
  • Decision boundaries
  • Decision function
  • Degrees of freedom (Dof)
  • Dendrogram
  • Design set
  • Detection
  • Discrete
    • algebraic Ricatti equation
    • Kalman filter (DKF)
    • Lyapunov equation
    • Ricatti equation
    • state
  • Discriminability
  • Discriminant function
    • generalized linear
    • linear
  • Dissimilarity
  • Distance
    • Bhattacharyya
    • Chernoff
    • cosine
    • Euclidean
    • inter/intraclass
    • interclass
    • intraclass
    • Mahalanobis
    • probabilistic
  • Distribution
    • Gamma
  • Drift
  • Dynamic stability

e

  • Editing
  • Elitism
  • Entropy
  • Ergodic Markov model
  • Error correction
  • Error covariance matrix
  • Error function
  • Error rate
  • Estimation
    • maximum a posteriori (MAP)
    • maximum likelihood
    • minimum mean absolute error (MMAE)
    • minimum mean squared error (MMSE)
    • minimum variance
  • Estimation loop
  • Evaluation
  • Evaluation set
  • Experiment design
  • Extended Kalman filter (EKF)

f

  • Face classification
  • Feature
  • Feature extraction
  • Feature reduction
  • Feature selection
    • generalized sequential forward
    • Plusl – take away r
    • selection of good components
    • sequential forward
  • Feed-forward neural network
  • Fisher approach
  • Fisher's linear discriminant
  • Fitness function
  • Fudge factor

g

  • Gain matrix
  • Gene
  • Generation
  • Generative topographic mapping
  • Genetic operators
  • Goodness of fit
  • Gradient ascent

h

  • Hidden Markov model (HMM)
  • Hidden neurons
  • Hierarchical clustering
  • Hill climbing algorithm
  • Histogramming
  • Holdout method

i

  • i.i.d.
  • Image classification
  • Image compression
  • Importance sampling
  • Incomplete data
  • Indicator variables
  • Infinite discrete-time model
  • Innovation(s)
    • matrix
  • Input vector

k

  • Kalman
    • filtering
    • form
  • Kalman filter
    • discrete
    • extended
    • iterated extended
    • linearized
  • Kalman gain matrix
  • Kernel
    • Gaussian
    • PCA(KPCA)
    • polynomial
    • radial basis function (RBF)
    • trick
  • K-means clustering
  • K-nearest neighbour rule
  • Kohonen map

l

  • Labeled data
  • Labelling
  • Labels
  • Lagrange multipliers
  • Latent variable
  • Learning
    • least squared error
    • non-parametric
    • parametric
    • perceptron
    • supervised
    • unsupervised
  • Learning data
  • Learning rate
  • Least squared error (LSE)
  • Leave-one-out method
  • Left–right model
  • Level estimation
  • Likelihood
    • function
    • ratio
  • Linear
    • dynamic equation
    • plant equation
    • state equation
    • system equation
  • Linear feature extraction
  • Linear feedback
  • Linear-Gaussian system
  • Linear system equation
  • Log-likelihood
  • Loss function

m

  • Mahalanobis distance
  • Mahalanobis distance classifier
  • MAP estimation
  • Mappings
  • Margin
  • Markov condition
  • Matched filtering
  • Maximum likelihood
  • Maximum likelihood estimation
  • Mean square error
  • Measure
    • divergence
    • Matusita
  • Minimum error rate
  • Minimum risk classification
  • Missing data
  • Mixture
    • of Gaussians
    • of probabilistic PCA
  • MMSE estimation
  • Mode estimation
  • Model selection
  • Monte Carlo simulation
  • Moving average models
  • Multidimensional scaling
  • Multiedit algorithm
  • Mutation

n

  • Nearest neighbour rule
  • Neuron
  • Nominal trajectory
  • Non-linear operation
  • Normalized
    • estimation error squared
    • importance weights
    • innovation squared

o

  • Objects
  • Observability
    • Gramian
    • matrix
  • Observation
  • Observer
  • Online estimation
  • Optimal filtering
    • Optimization criterion
    • Outlier clusters
    • Outliers
    • Overfitting

p

  • Parameter vector
  • Particle filter
  • Particle filtering
  • Particles
  • Parzen estimation
  • Perceptron
  • Periodogram
  • Place coding
  • Predicted measurement
  • Principal
    • component analysis
    • components
    • directions
  • Principle of orthogonality
  • Probabilistic dependence
  • Probability
    • posterior
    • prior
  • Probability density
    • conditional
    • posterior
  • Process noise
  • Proposal density
  • Population

q

  • Quadratic decision function
  • Quantization errors

r

  • Random walk
  • Regression curve
  • Regularization
  • Reject rate
  • Rejection
    • class
  • Resampling by selection
  • Residual(s) 100
  • Retrodiction
  • Ricatti loop
  • Risk
    • average
    • conditional
  • Robust error norm
  • Robustness
  • Root mean square (RMS) 390

s

  • Sammon mapping
  • Sample
    • covariance
    • mean
  • Sampling
  • Scatter matrix
    • between-scatter matrix
    • within-scatter matrix
  • Selection
  • Self-organizing map
  • Signal-to-noise ratio
  • Silhouette classification
  • Single sample processing
  • Smoothing
  • Stability
  • State
    • augmentation
    • estimation
      • offline
      • online
    • mixed
  • Statistical linearization
  • Steady state
  • Steepest ascent
  • Stress measure
  • Subspace
    • dimension
    • structure
  • Sum of squared differences (SSD)
  • Support vector
  • System
    • identification
    • noise
    • matrix

T

  • Target vector
  • Test set
  • Topology
  • Training set
  • Trait
  • Transfer function
  • Transition probability density

u

  • Unbiased
    • absolutely
  • Unit cost
  • Unlabeled data
  • Untrained mapping

v

  • Validation set
  • Viterbi algorithm

w

  • Weak classifier
  • Weak learners
  • Weak hypothesis
  • Weight distribution
  • White random sequence
  • Winning neuron
  • Wishart distribution
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