Raport de cercetare - Lorentz JÄNTSCHI

o SVMs application to protein secondary structure prediction
Protein secondary structure prediction
• Different levels of structural organization in proteins
• A problem of central importance in structural biology
• Different measures of prediction accuracy
State of the art
• Choice of the predictors
• Building blocks and architecture of the main prediction methods
Implementation of multi-class SVMs
• Models implemented
• Training algorithm
• Dedicated RBF kernel
• Computation opf weighting vector θ
• Experimental results
Future work
o Multiple kernel learning and HM-SVM for bioinformatic applications
Support Vector Machines (SVMs)
Handling Non-Linearity with Kernels
SVMs as Perceptrons
Application: Predicting Protein Subcellular Localization
Multiple Kernel Learning (MKL)
Large Margin MKL Model
Optimization for MKL
Normalization of Kernels
Multiclass Multiple Kernel Learning
Application: Predicting Protein Subcellular Localization
o Semi-Supervised Learning
Why Semi-Supervised Learning?
Why and How Does SSL Work?
Generative Models
The Semi-Supervised SVM (S 3 VM)
Graph-Based Methods
Further Approaches (Co-Training, Transduction)
o Multi-Valued and UB Neurons - I
Why we need the complex valued neurons?
A classical Minsky-Papert’s limitation
Is it possible to learn XOR and Parity n functions using a single neuron?
Multi-Valued and Universal Binary Neurons (MVN and UBN)
Multi-valued mappings
Traditional approaches to learn the multiple-valued mappings on a neuron
Sigmoidal neurons: limitations
Multi-Valued Neuron (MVN)
Multi-valued mappings and multiple-valued logic
Discrete-Valued (k-valued) Activation Function
Multiple-Valued (k-valued) Threshold Functions
Learning Algorithm for the Discrete MVN with the Error-Correction
Learning Rule
Continuous-Valued Activation Function
Learning Algorithm for the Continuous MVN with the Error Correction
Learning Rule
A role of the factor 1/(n+1) in the Learning Rule
Self-Adaptation of the Learning Rate
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