另有方法是統(tǒng)計(jì)變異程度

1、國(guó)立雲(yún)林科技大學(xué)National Yunlin University of Science and TechnologyThe Evolving TreeAnalysis and ApplicationsAdvisor : Dr. HsuPresenter : Zih-Hui LinAuthor:Jussi Pakkanen, Jukka Iivarinen, and Erkki Oja,Intelligent Database Systems Lab忍 J，展?護(hù)lEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY2006Outl

2、ine Motivation Objective ETree ETree-Analysis Experiments ConclusionsWN.Y.U.S.T.IMI.M.Motivation Some of its intrinsic features make it unsuitable for analyzing very large scale problems. This converts complexity control from a global problem to a local one which is simpler.Intelligent Database Syst

3、ems Lab6rI.M.Objective We have analyzed and compared the ETree against many different systems.Intelligent Database Systems Lab6rI.M.ETreeTraining data咖、o，QO/ oX.1)Find the BMU using the search tree2) Update the leaf node locations using the SOM training formulas substituting tree distanee for grid d

4、istanee.w誼 + 1) = w誼)+ g()x(t) - w,f)九?=&exp (_?；*；e/oXMu；/ ；、/3) Increment the BMUs hit counter.4) If the counter reaches the splitting threshold, split the node.Intelligent Database Systems Lab6rI.M.O、/ 1)2)ETree- How to controlling the growthAt the end of each epoch set bit +1) = 7 bi(t) for all

5、nodes z, where 7 日0)1.If the tree has grown less than a specified amount (e.g.，5%) since the last epoch, stop training.oes os ass 0.8 a75 az o.$5 ox 055 as 0.45 m 0塑 as 0.25 0.2 0.15 ai Regularization coefficientaej uoueoESSEo: 1I.M.ETree- Removing LayersRemoved lavers One beneficial feature of most

6、 neural networks is graceful degradation 0.20.10I.M.ETree- better Search for the BMU At every layer we keep the n best subbranches instead of only one.gular BMUTree distances, 2 BMUsTree distancergptimal BML Regular 2 BMUs Optimal BMUI.M. WMhoutPCA Without PCA (optical BMU PCA PCA(

7、opCmaiBMU)Orwal PcaRandomETree- Child Node Initialization The first perturbs the child nodes ponent analysis (PCA).Standard devia nee of subtree sizeIntelligent Database Systems LabI.M.ETree- Optimizing the Leaf Node Locations1) First, we map all training vectors to leaf nodes using the

8、established BMU search.2) Then we move the leaf nodes to the center of mass of their respective data vectors.Large dataset1ag EE EMek mam0.1HomoQonMUS texturebfcntfwrittin digt*11001000900aoo7006005004003002001000EtroeEtree kmaans(b)K-rrieansI.M.Visualization experimentsOlDQQ 3Gau”CIOO&4O-0.0-Q/tCla

9、ssGZIAS&1 cClAS&.0.30 5-0 5ETreeLeaf nodes-o sGOClavu 36 s 1GiaO 5-O.K1Cea* 17Ogar 4SOMKm eans0=50 5-0.6廠小 02) O GPmf-Q-o*050.5-OSOS-0.50.5-05Claool *1datavectorClewi-tq0 5O-O.&-1Q1Class 1OOSooO05CkJt;vtCIUW4C3&6ClassiO；as-OBOS0.5O.S4-SOB06Intelligent Database Systems Lab 厲I.M.Quality of clustering0

10、.5OQuantization error0.45 一0.40.35without the search treeInverse quantization errorDavies-Bouldin indexDBI =Imaxn 翕 i#j徑,（q）+ sg八 k sec）丿Intelligent Database Systems Lab6rWN.Y.U.S.T.IMConclusions ETree5 s performance is quite close to classical, nonhierarchical algorithms but it is noticeably faster, ETree makes implementation and application easier