Neural networks for data mining Eric Postma MICC-IKAT Universiteit Maastricht.
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Neural networks Neural networks for data mining for data mining Eric Postma Eric Postma MICC-IKAT MICC-IKAT Universiteit Maastricht Universiteit Maastricht
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Transcript of Neural networks for data mining Eric Postma MICC-IKAT Universiteit Maastricht.
Neural networksNeural networksfor data miningfor data mining
Eric PostmaEric Postma
MICC-IKATMICC-IKAT
Universiteit MaastrichtUniversiteit Maastricht
OverviewOverview
Introduction: The biology of neural networks• the biological computer
• brain-inspired models
• basic notions
Interactive neural-network demonstrations• Perceptron
• Multilayer perceptron
• Kohonen’s self-organising feature map
• Examples of applications
Two types of learningTwo types of learning
• Supervised learningSupervised learning• curve fitting, surface fitting, ...curve fitting, surface fitting, ...
• Unsupervised learningUnsupervised learning• clustering, visualisation...clustering, visualisation...
Fitting a surface to four pointsFitting a surface to four points
RegressionRegression
The history of neural networksThe history of neural networks
• A powerful metaphorA powerful metaphor
• Several decades of theoretical analyses led Several decades of theoretical analyses led to the formalisation in terms of statisticsto the formalisation in terms of statistics
• Bayesian frameworkBayesian framework
• We discuss neural networks from the We discuss neural networks from the original metaphorical perspective original metaphorical perspective
(Artificial) neural networks(Artificial) neural networks
The digital computer The digital computer versusversus
the neural computerthe neural computer
The Von Neumann architectureThe Von Neumann architecture
The biological architectureThe biological architecture
Digital versus biological computersDigital versus biological computers
5 distinguishing properties5 distinguishing properties• speedspeed• robustness robustness • flexibilityflexibility• adaptivityadaptivity• context-sensitivitycontext-sensitivity
Speed: Speed: The “hundred time steps” argumentThe “hundred time steps” argument
The critical resource that is most obvious is The critical resource that is most obvious is time. Neurons whose basic computational time. Neurons whose basic computational speed is a few milliseconds must be made to speed is a few milliseconds must be made to account for complex behaviors which are account for complex behaviors which are carried out in a few hudred milliseconds carried out in a few hudred milliseconds (Posner, 1978). This means that (Posner, 1978). This means that entire complex entire complex behaviors are carried out in less than a hundred behaviors are carried out in less than a hundred time steps.time steps.
Feldman and Ballard (1982)Feldman and Ballard (1982)
Graceful DegradationGraceful Degradation
damage
performance
Flexibility: the Flexibility: the NeckerNecker cube cube
vision = constraint satisfactionvision = constraint satisfaction
And sometimes plain search…And sometimes plain search…
AdaptivitiyAdaptivitiy
processing implies learningprocessing implies learning
in biological computers in biological computers
versus versus
processing does not imply learningprocessing does not imply learning
in digital computersin digital computers
Context-sensitivity: patternsContext-sensitivity: patterns
emergent propertiesemergent properties
Robustness and context-sensitivityRobustness and context-sensitivitycoping with noisecoping with noise
The neural computerThe neural computer
• Is it possible to develop a model after the Is it possible to develop a model after the natural example?natural example?
• Brain-inspired models:Brain-inspired models:• models based on a restricted set of structural en models based on a restricted set of structural en
functional properties of the (human) brainfunctional properties of the (human) brain
The Neural Computer (structure)The Neural Computer (structure)
Neurons, Neurons, the building blocks of the brainthe building blocks of the brain
Synapses,Synapses,the basis of learning and memory the basis of learning and memory
Learning:Learning: Hebb Hebb’s rule’s ruleneuron 1 synapse neuron 2
Forgetting in neural networksForgetting in neural networks
ConnectivityConnectivityAn example:An example:The visual system is a The visual system is a feedforward hierarchy of feedforward hierarchy of neural modules neural modules
Every module is (to a Every module is (to a certain extent) certain extent) responsible for a certain responsible for a certain functionfunction
(Artificial) Neural Networks(Artificial) Neural Networks
• NeuronsNeurons• activityactivity• nonlinear input-output functionnonlinear input-output function
• Connections Connections • weightweight
• LearningLearning• supervisedsupervised• unsupervisedunsupervised
Artificial NeuronsArtificial Neurons
• input (vectors)input (vectors)• summation (excitation)summation (excitation)• output (activation)output (activation)
i
Input-output functionInput-output function
• nonlinear function:nonlinear function:
e
f(e)
f(x) = 1 + e -x/a
1
a 0
a
Artificial Connections Artificial Connections (Synapses)(Synapses)
• wwABAB
• The weight of the connection from neuron The weight of the connection from neuron AA to to neuron neuron BB
A BwAB
Learning in the PerceptronLearning in the Perceptron• Delta learning ruleDelta learning rule
• the difference between the desired output the difference between the desired output ttand the actual output and the actual output oo, , given input given input xx
• Global error E Global error E • is a function of the differences between the is a function of the differences between the
desired and actual outputsdesired and actual outputs
Minsky and Papert’s Minsky and Papert’s connectedness connectedness argumentargument
The history of the PerceptronThe history of the Perceptron
• Rosenblatt (1959)Rosenblatt (1959)
• Minsky & Papert (1961)Minsky & Papert (1961)
• Rumelhart & McClelland (1986)Rumelhart & McClelland (1986)
The multilayer perceptronThe multilayer perceptron
input
one or more hidden layers
output
Training the MLPTraining the MLP• supervised learningsupervised learning
• each training pattern: input + desired output each training pattern: input + desired output • in each in each epochepoch: present all patterns : present all patterns • at each presentation: adapt weightsat each presentation: adapt weights• after many epochs convergence to a local minimumafter many epochs convergence to a local minimum
phoneme recognition with a MLPphoneme recognition with a MLP
input: frequencies
Output:pronunciation
Non-linear decision boundariesNon-linear decision boundaries
Compression with an MLPCompression with an MLPthe the autoencoderautoencoder
Restricted Boltzmann machines (RBMs)Restricted Boltzmann machines (RBMs)
Preventing OverfittingPreventing Overfitting
GENERALISATION GENERALISATION = performance on test set= performance on test set
• Early stoppingEarly stopping• Training, Test, and Validation setTraining, Test, and Validation set• kk-fold cross validation-fold cross validation
• leaving-one-out procedureleaving-one-out procedure
Image Recognition with the MLPImage Recognition with the MLP
Other ApplicationsOther Applications
• PracticalPractical• OCROCR• financial time seriesfinancial time series• fraud detectionfraud detection• process controlprocess control• marketingmarketing• speech recognitionspeech recognition
• TheoreticalTheoretical• cognitive modelingcognitive modeling• biological modelingbiological modeling
Derivation of the delta learning ruleDerivation of the delta learning rule
Target output
Actual output
h = i
Sigmoid functionSigmoid function
• May also be theMay also be the tanhtanh functionfunction • (<-1,+1> (<-1,+1> instead of instead of <0,1>)<0,1>)
• DerivativeDerivative f’(x) = f(x) [1 – f(x)] f’(x) = f(x) [1 – f(x)]
Derivation generalized delta ruleDerivation generalized delta rule
AdaptationAdaptation hidden-output hidden-output weightsweights
AAdaptationdaptation input-hidden input-hidden weightsweights
Forward Forward andand Backward Propagation Backward Propagation
Decision boundaries of PerceptronsDecision boundaries of Perceptrons
Straight lines (surfaces), linear separable
Decision boundaries of MLPsDecision boundaries of MLPs
Convex areas (open or closed)
Decision boundaries of MLPs Decision boundaries of MLPs
Combinations of convex areas
Learning and representing Learning and representing similaritysimilarity
Alternative conception of neuronsAlternative conception of neurons
• Neurons do not take the weighted sum of their Neurons do not take the weighted sum of their inputs (as in the perceptron), but measure the inputs (as in the perceptron), but measure the similarity of the weight vector to the input similarity of the weight vector to the input vectorvector
• The activation of the neuron is a measure of The activation of the neuron is a measure of similarity. The more similar the weight is to the similarity. The more similar the weight is to the input, the higher the activationinput, the higher the activation
• Neurons represent “prototypes”Neurons represent “prototypes”
Prototypes forPrototypes for preprocessing preprocessing
Kohonen’s SOFMKohonen’s SOFM(Self Organizing Feature Map)(Self Organizing Feature Map)
• Unsupervised learningUnsupervised learning• Competitive learningCompetitive learning
output
input (n-dimensional)
winner
Competitive learningCompetitive learning
• Determine the winner (the neuron of which Determine the winner (the neuron of which the weight vector has the smallest distance the weight vector has the smallest distance to the input vector)to the input vector)
• Move the weight vector Move the weight vector ww of the winning of the winning neuron towards the input neuron towards the input ii
Before learning
i
w
After learning
i w
Kohonen’s ideaKohonen’s idea
• Impose a topological order onto the Impose a topological order onto the competitive neurons (e.g., competitive neurons (e.g., rectangular map)rectangular map)
• Let neighbours of the winner share Let neighbours of the winner share the “prize” (The “postcode lottery” the “prize” (The “postcode lottery” principle.)principle.)
• After learning, neurons with similar After learning, neurons with similar weights tend to cluster on the mapweights tend to cluster on the map
Topological orderTopological order
neighbourhoodsneighbourhoods• SquareSquare
• winner (red)winner (red)• Nearest neighboursNearest neighbours
• HexagonalHexagonal• Winner (red)Winner (red)• Nearest neighboursNearest neighbours
A simple exampleA simple example
• A topological map of 2 x 3 neurons A topological map of 2 x 3 neurons and two inputsand two inputs
2D input
input
weights
visualisation
Input patterns Input patterns (note the 2D distribution)(note the 2D distribution)
Another exampleAnother example
• Input: uniformly randomly distributed pointsInput: uniformly randomly distributed points
• Output: Map of 20Output: Map of 2022 neurons neurons
• TrainingTraining• Starting with a large learning rate and Starting with a large learning rate and
neighbourhood size, both are gradually decreased neighbourhood size, both are gradually decreased to facilitate convergenceto facilitate convergence
Dimension reductionDimension reduction
3D input2D output
Adaptive resolutionAdaptive resolution
2D input2D output
Application of SOFMApplication of SOFM
Examples (input) SOFM after training (output)
Visual features (biologically plausible)Visual features (biologically plausible)
• Principal Components Analysis (PCA)Principal Components Analysis (PCA)
pca1pca2
pca1
pca2
Projections of data
Relation with statistical methods 1Relation with statistical methods 1
Relation with statistical methods 2Relation with statistical methods 2• Multi-Dimensional Scaling (MDS)Multi-Dimensional Scaling (MDS)• Sammon MappingSammon Mapping
Distances in high-dimensional space
