Presentation on theme: "Artificial Neural Networks And XML"— Presentation transcript:
1Artificial Neural Networks And XML Presented by : M. EftekhariAdvisor : Dr. S. Astaneh
2Outlines Xml-Based format for trained Neural Network definition MotivationsNeural Network Markup Language (NNML)Decomposition of Neural Nets ModelThe neural model description in NNMLProcessing of NNML documentsPMML….IntroductionFrom biological Artificial Neural NetsInherent capacitiesThe Distributed Training Environment (DTE)Why distributed environment? (Motivation)FeaturesJOONE
5A simple Artificial Neuron Out put connections are similar to axonsSum simulates thedendritesw0=fActivation functionHas the role of events thatOccur in a real neuron of brainw1w2Weights are similar to synapsex1x2The learning is the process of updating weights
6Inherent capacities (Intro.) The neurons are parallel inside each other due to inherent structure of Neural Network.When a network learns, it works as a autonomous mechanism (speech part of brain).A central mechanism coordinates, schedules and these self-organize parts. (may be ensemble of parts needed)So ANNs can be distributed. A learnt ANNs can be shared to use by other applications.
8Why distributed environment? (Motivation) use a neural net to resolve complex jobs is not sufficient.For complex problem, net can fall onto a local minima without finding the best resultswhat must be developed is a mechanism to train many neural nets in parallel on the same problem, on several machines, governing the whole process from a central point.
9Java Object Oriented Neural Engine Joone is a FREE neural net framework to create, train and test neural netsDistributed Training Environment to train in parallel mode many neural networks to find the fittest one for a given problem.
10Features Centralized control The Final results are logged into a XML file to permit to analyze the results from a custom program/scriptThe training process scale almost linearly adding more machinesNo manual configuration needed to add or remove machinesPossibility to add or remove machines dynamically during the training processThe overall process controlled by XML parameters
11Xml-Based format for trained Neural Network definition
12Motivations unified way for neural network model definition Interchanging neural models as well as documentationstore and manipulating them independently from the simulation system that produced it.The development of the neural-based Web services
13Neural Network Markup Language (NNML) XML-based language (Neural Network Markup Language) for the neural network model description.NNML as an interface between various software systems concerning neural networks (see Fig of next slide)NNML causes separation of neural networks generators, interpreters, tools for visualization and knowledge extraction (see Fig of next slide)Applied to the distribution of the neural network modelsIntegrating the powerful simulation systems like Matlab with Web interface
16The neural model description in NNML The problem and model purpose (Task)Data dictionaryData preprocessorNeural networkPostprocessorAuxiliary information about the model.
17The neural model description in NNML A simple neuron Which objectForm WhichlayerlayerObj
18Processing of NNML documents Generation and training by means of the neural network simulator.Creating hierarchical model by the interface module on the basis of internal representation.Methods of any XML parser are called, object tree of the model are constructedNNML file is generated.For loading of the ready NNML file, actions are performed in the reverse order.
21IntroductionThe PMML is a set of Document Type Descriptions (DTDs) specified in XML.The first version (1.0) was provided in July by the Data Mining Group (DMG,A Markup Language for Predictive modeling, but not only restricted to this field.Support only Back propagation Nets despite of previous introduced Method.
22PMML (Contd.)The PMML 1.1 definition includes DTDs for the following types of models:1. Naïve bayes2. Regression Models3. Decision trees4. Center and distribution based clusters5. Sequence and association rules6. neural nets
23Advantages of PMML:Removes the issues of incompatibility between applications and proprietary formats.DTDs support proprietary extensions to allow for enriched information storage for specialized tools.Previous solutions to the problem of sharing data models were incorporated into custom-built systems, and thus exchange of models with an application outside of the system was virtually impossible
24Advantages of PMML For example, it allows users: (sharing the data) To generate data models using one vendor application.Use other vendor application to analyze.Another to evaluate the models.Another vendor application to visualize the model.
25PMML (Contd.) The PMML describes the models using eight modules: HeaderData Dictionary schemaData Mining schemaPredictive model schemaDefinition for predictive modelsDefinition for ensemble of modelsRules for selecting and combining models and ensembles of modelsRules for exception handling
26The role of PMML in the Knowledge Discovery process.
27PMML (Contd.) Using PMML to model Association Rules <?xml version="1.0" ?><PMML version="1.1"><Header copyright="www.dmg.org" description="sample model for association rules"/><DataDictionary numberOfFields="1" > <DataField name="item" optype="categorical" /> </DataDictionary><AssociationModel><AssocInputStats numberOfTransactions="4" numberOfItems="3"minimumSupport="0.6" minimumConfidence="0.5"numberOfItemsets="3" numberOfRules="2"/><!-- We have three items in our input data --><AssocItem id="1" value="Cracker" /><AssocItem id="2" value="Coke" /><AssocItem id="3" value="Water" /><!-- and two frequent itemsets with a single item --><AssocItemset id="1" support="1.0" numberOfItems="1"><AssocItemRef itemRef="1" /></AssocItemset><AssocItemset id="2" support="1.0" numberOfItems="1"><AssocItemRef itemRef="3" /><!-- and one frequent itemset with two items. --><AssocItemset id="3" support="1.0" numberOfItems="2"><AssocItemRef itemRef="1" /><AssocItemRef itemRef="3" /><!-- Two rules satisfy the requirements --><AssocRule support="1.0" confidence="1.0" antecedent="1" consequent="2" /><AssocRule support="1.0" confidence="1.0" antecedent="2" consequent="1" /></AssociationModel></PMML>
28PMML The General Web Architecture Application interfacesWeb WarehouseMaterialize and manages usefulInformation on webA software that facilitates the process ofContent extraction
29PMML and ANNs (DTD)<!ELEMENT NeuralInput (Extension*, ( NormContinuous | NormDiscrete )) ><!ATTLIST NeuralInput id %NN-NEURON-ID; #REQUIRED >NN-NEURON-ID is just a string which identifies a neuron
42PMML and ANNs <MiningSchema> </MiningSchema> <MiningField name="gender"/><MiningField name="no of claims"/><MiningField name="domicile"/><MiningField name="age of car"/><MiningField name="amount of claims" usageType="predicted"/></MiningSchema>
43PMML and ANNs <NeuralInputs numberOfInputs="10"> <NeuralInput id="0"><DerivedField><NormContinuous field="age of car"><LinearNorm orig="0.01" norm="0"/><LinearNorm orig=" " norm="0.5"/><LinearNorm orig="11.44" norm="1"/></NormContinuous></DerivedField></NeuralInput>
44PMML and ANNs <NeuralInput id="1"> <DerivedField> <NormDiscrete field="gender" value=" male"/></DerivedField></NeuralInput>…. To 9