Identification and Neural Networks

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• Identification and Neural Networks

G. Horváth
I S R G
Department of Measurement and Information Systems
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Modular networks

• Why modular approach
• Motivations
• Biological
• Learning
• Computational
• Implementation

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Motivations

• Biological
• Biological systems are not homogenous
• Functional specialization
• Fault tolerance
• Cooperation, competition
• Scalability
• Extendibility

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Motivations

• Complexity of learning (divide and conquer)
• Training of complex network (many layers)
• layer by layer learning
• Speed of learning
• Catastrophic interference, incremental learning
• Mixing supervised and unsupervised learning
• Hierarchical knowledge structure

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Motivations

• Computational
• The capacity of a network
• The size of the network
• Catastrophic interference
• Generalization capability vs network complexity

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Motivations

• Implementation (hardware)
• The degree of parallelism
• Number of connections
• The length of physical connections
• Fan out

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Modular networks

• What modules
• The modules are disagree on some inputs
• every module solves the same, whole problem,
• different ways of solutions (different modules)
• every module solves different tasks (sub-tasks)
• task decomposition (input space, output space)

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Modular networks

• How combine modules
• Cooperative modules
• simple average
• weighted average (fixed weights)
• optimal linear combination (OLC) of networks
• Competitive modules
• majority vote
• winner takes all
• Competitive/cooperative modules
• weighted average (input-dependent weights)
• mixture of experts (MOE)

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Modular networks

• Construct of modular networks
• Task decomposition, subtask definition
• Training modules for solving subtasks
• Integration of the results
• (cooperation and/or competition)

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Modular networks

• Cooperative networks
• Ensemble (average)
• Optimal linear combination of networks
• Disjoint subtasks
• Competitive networks
• Ensemble (vote)
• Competitive/cooperative networks
• Mixture of experts

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Cooperative networks

• Ensemble of cooperating networks
  (classification/regression)
• The motivation
• Heuristic explanation
• Different experts together can solve a problem
  better
• Complementary knowledge
• Mathematical justification
• Accurate and diverse modules

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Ensemble of networks

• Mathematical justification
• Ensemble output
• Ambiguity (diversity)
• Individual error
• Ensemble error
• Constraint

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Ensemble of networks

• Mathematical justification (contd)
• Weighted error
• Weighted diversity
• Ensemble error
• Averaging over the input distribution
• Solution Ensemble of accurate and diverse
  networks

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Ensemble of networks

• How to get accurate and diverse networks
• different structures more than one network
  structure (e.g. MLP, RBF, CCN, etc.)
• different size, different complexity networks
  (number of hidden units, number of layers,
  nonlinear function, etc.)
• different learning strategies (BP, CG, random
  search,etc.) batch learning, sequential learning
• different training algorithms, sample order,
  learning samples
• different training parameters
• different starting parameter values
• different stopping criteria

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Linear combination of networks
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Linear combination of networks

• Computation of optimal coefficients
•        ? simple average
•         , k depends on
  the input for different input domains different
  network (alone gives the output)
• optimal values using the constraint
• optimal values without any constraint
•   Wiener-Hopf equation

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Task decomposition

• Decomposition related to learning
• before learning (subtask definition)
• during learning (automatic task decomposition)
• Problem space decomposition
• input space (input space clustering, definition
  of different input regions)
• output space (desired response)

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Task decomposition

• Decomposition into separate subproblems
• K-class classification K two-class
  problems (coarse decomposition)
• Complex two-class problems smaller
  two-class problems (fine decomposition)
• Integration (module combination)

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Task decomposition

• A 3-class problem

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Task decomposition

• 3 classes

2 small classes
2 small classes
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Task decomposition

• 3 classes

2 small classes
2 small classes
2 classes
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Task decomposition

• 3 classes

2 small classes
2 small classes
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Task decomposition
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Task decomposition

• A two-class problem
• decomposed into
• subtasks

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Task decomposition
M22
M21

• M11

M12
AND
AND
OR
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Task decomposition

• M11

MIN

• M12

C1
MAX
Input
M21
MIN
M22
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Task decomposition

• Training set decomposition
• Original training set
• Training set for each of the (K) two-class
  problems
• Each of the two-class problems are divided into
  K-1 smaller two-class problems using an inverter
  module really (K-1)/2 is enough

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Task decomposition

• A practical example Zip code recognition

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Task decomposition

• Zip code recognition (handwritten character
  recognition) modular solution

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Mixture of Experts (MOE)
µ
S
g1
g2
gM
Gating network
?M
µ1
Expert M
Expert 2
Expert 1
x
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Mixture of Experts (MOE)

• The output is the weighted sum of the outputs of
  the experts
• is the parameter of the i-th expert
• The output of the gating network softmax
  function
• is the parameter of the gating network

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Mixture of Experts (MOE)

• Probabilistic interpretation
• The probabilistic model with true parameters
• a priori probability

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Mixture of Experts (MOE)

• Training
• Training data
• Probability of generating output from the input
• The log likelihood function (maximum likelihood
  estimation)

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Mixture of Experts (MOE)

• Training (contd)
• Gradient method
• The parameter of the expert network
• The parameter of the gating network

and
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Mixture of Experts (MOE)

• Training (contd)
• A priori probability
• A posteriori probability

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Mixture of Experts (MOE)

• Training (contd)
• EM (Expectation Maximization) algorithm
• A general iterative technique for maximum
  likelihood estimation
• Introducing hidden variables
• Defining a log likelihood function
• Two steps
• Expectation of the hidden variables
• Maximization of the log likelihood function

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EM (Expectation Maximization) algorithm

• A simple example estimating means of k (2)
  Gaussians

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EM (Expectation Maximization) algorithm

• A simple example estimating means of k (2)
  Gaussians
• hidden variables for every observation,
• (x(l), zi1, zi2)
• likelihood function
• Log likelihood function
• expected value of with given

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Mixture of Experts (MOE)

• A simple example estimating means of k (2)
  Gaussians
• Expected log likelihood function
• where
• The estimate of the means

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Mixture of Experts (MOE)

• Applications
• Simple experts linear experts
• ECG diagnostics
• Mixture of Kalman filters
• Discussion comparison to non-modular
  architecture

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Support vector machines

• A new approach
• Gives answers for questions not solved using the
  classical approach
• The size of the network
• The generalization capability

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Support vector machines

• Classification

Optimal hyperplane
Classical neural learning
Support Vector Machine
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VC dimension
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Structural error minimization
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Support vector machines

• Linearly separable two-class problem
• separating hyperpalne

Optimal hyperplane
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Support vector machines

• Geometric interpretation

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Support vector machines

• Criterion function, Lagrange function
• a constrained optimization problem
• conditions
• dual problem
• support vectors optimal hyperplane

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Support vector machines

• Linearly nonseparable case
• separating hyperplane
• criterion function
• Lagrange function
• support vectors optimal hyperplane

Optimal hyperplane
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Support vector machines

• Nonlinear separation
• separating hyperplane
• decision surface
• kernel function
• criterion function

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Support vector machines

• Examples of SVM
• Polynomial
• RBF
• MLP

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Support vector machines

• Example polynomial
• basis functions
• kernel function

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SVR (classification)
Separable samples
Not separable samples
Constraint
Constraint
Minimize
Minimize
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SVR (regression)
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SVR (regression)

Constraints
Minimize
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SVR (regression)

• Lagrange function
• dual problem
• constraints
• support vectors
• solution

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SVR (regression)
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SVR (regression)
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SVR (regression)
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SVR (regression)
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Support vector machines

• Main advantages
• generalization
• size of the network
• centre parameters for RBF
• linear-in-the-parameter structure
• noise immunity

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Support vector machines

• Main disadavantages
• computation intensive (quadratic optimization)
• hyperparameter selection
• VC dimension (classification)
• batch processing

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Support vector machines

• Variants
• LS SVM
• basic criterion function
• Advantages easier to compute
• adaptivity,

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Mixture of SVMs

• Problem of hyper-parameter selection for SVMs
• Different SVMs, with different hyper-parameters
• Soft separation of the input space

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Mixture of SVMs
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Boosting techniques

• Boosting by filtering
• Boosting by subsampling
• Boosting by reweighting

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Boosting techniques

• Boosting by filtering

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Boosting techniques

• Boosting by subsampling

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Boosting techniques

• Boosting by reweighting

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Other modular architectures
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Other modular architectures
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Other modular architectures

• Modular classifiers
• Decoupled modules
• Hierarchical modules
• Network ensemble (linear combination)
• Network ensemble (decision, voting)

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Modular architectures