My Research History on NN, FS, and SVM. â¢ Neural Networks (1988 ... SVMs outperform conventional classifiers. ...... Back propagation algorithm (BP). â¢ Support ...
Support Vector Machines for Pattern Classification Shigeo Abe Graduate School of Science and Technology Kobe University Kobe, Japan
My Research History on NN, FS, and SVM • Neural Networks (1988) – Convergence characteristics of Hopfield networks – Synthesis of multilayer neural networks
• Fuzzy Systems (1992) – Trainable fuzzy classifiers – Fuzzy classifiers with ellipsoidal regions
• Support Vector Machines (1999) – Characteristics of solutions – Multiclass problems
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
Multilayer Multilayer Neural Neural Networks Networks Neural networks are trained to output the target values for the given input. Class 1
N.N.
Class 1 1 0 Class 2
Class 2
Multilayer Multilayer Neural Neural Networks Networks Indirect decision functions: decision boundaries change as the initial weights are changed. Class 1
N.N.
Class 1 1 0 Class 2
Class 2
Support Support Vector Vector Machines Machines Direct decision functions: decision boundaries are determined to minimize the classification error of both training data and unknown data. Class 1
SVM
If D(x) > 0, Class 1 Otherwise, Class 2
Maximum margin Class 2
Optimal hyperplane (OHP) D(x) = 0
Summary Summary • When the number of training data is small, SVMs outperform conventional classifiers. • By maximizing margins performance of conventional classifiers can be improved.
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
Architecture Architecture of of SVMs SVMs • Formulated for twoclass classification problems • Map the input space into the feature space • Determine the optimal hyperplane in the feature space
Class 2
g(x)
Class 1 OHP Input Space
Feature Space
Types Types of of SVMs SVMs • Hard margin SVMs – linearly separable in the feature space – maximize generalization ability
• Soft margin SVMs – Not separable in the feature space – minimize classification error and maximize generalization ability
Class 2 Class 1
• L1 soft margin SVMs (commonly used) • L2 soft margin SVMs
Feature Space
Hard Hard Margin Margin SVMs SVMs We determine OHP so that the generalization region is maximized:
Margin δ
Generalization region
maximize δ subject to wtg(xi ) + b ≧ 1 for Class 1 − wtg(xi ) − b ≧ 1 for Class 2
Class 1 yi = 1 Class 2 yi = − 1
+ side
Combining the two: yi (wtg( xi ) + b) ≧ 1
OHP D(x) = wtg(xi ) + b = 0
Hard Hard Margin Margin SVM SVM (2) (2) The distance from x to OHP is given by yi D(x) /w. Thus all the training data must satisfy
Margin δ
yi D(x) /w ≧ δ . Imposing w δ = 1, the problem is to minimize w2/2 subject to yi D(xi) ≧ 1
for i = 1,…,M.
Generalization region
Class 1 yi = 1 Class 2 yi = − 1
+ side
OHP D(x) = wtg(xi ) + b = 0
Soft Soft Margin Margin SVMs SVMs If the problem is nonseparable, we introduce slack variables ξi. Minimize w2/2 + C/p Σi=1,M ξip subject to yi D(xi) ≧ 1 − ξi where C: margin parameter, p = 1: L1 SVM, = 2: L2 SVM
Margin δ
Class 1 yi = 1
1> ξi > 0 Class 2 yi = − 1
ξk >1 + side
OHP D(x) = 0
Conversion Conversion to to Dual Dual Problems Problems Introducing the Lagrange multiplies αi and βi, Q = w2/2 +C/p Σi ξip −Σi αi(yi(wtg(xi) + b) −1+ξi) [−Σ −Σi βiξi ] The KarushKuhnTacker (KKT) optimality conditions: ∂Q/∂ ∂w = 0,∂ ∂Q/∂ ∂b = 0,∂ ∂Q/∂ ∂ξi = 0, αi > 0, βi > 0 KKT complementarity conditions αi(yi(wtg(xi) + b) −1+ξi) = 0, [(βiξi = 0 ]. When p = 2, terms in [ ] are not necessary.
Dual Dual Problems Problems of of SVMs SVMs L1 SVM Maximize Σ Σi αi − C/2 Σi,j αi αj yi yj g(xi )tg(xj) subject to Σ Σi yi αi = 0, C ≧ αi ≧0. L2 SVM Maximize Σ Σi αi − C/2 Σi,j αi αj yi yj (g(xi )tg(xj) + δij /C) subject to Σ Σi yi αi = 0, αi ≧0, where δij : 1 for i = j and 0 for i ≠ j.
KKT KKT Complementarity Complementarity Condition Condition For L1 SVMs, from αi(yi(wtg(xi) + b) −1+ξi) = 0, − αi) ξi = 0, there are three cases for αi : βiξi = (C− 1. αi = 0. Then ξi = 0. Thus xi is correctly classified, 2. 0 < αi < C. Then ξi = 0, and wtg(xi) + b = yi, 3. αi = C. Then ξi ≧ 0. Training data xi with αi > 0 are called support vectors and those with αi = C are called bounded support vectors. The resulting decision function is given by D(x) = Σi αiyig(xi)t g(x) + b.
Kernel Kernel Trick Trick Since mapping function g(x) appears in the form of g(x)tg(x’), we can avoid treating the variables in the feature space by introducing the kernel: H(x,x’) = g(x)tg(x’). The following kernels are commonly used: 1. Dot product kernels: H(x,x’) = xtx’ 2. Polynomial kernels: H(x,x’) = (xtx’+1)d 3. RBF kernels : H(x,x’) = exp(− − γ x − x’2)
Summary Summary • The global optimum solution by quadratic programming (no local minima). • Robust classification for outliers is possible by proper value selection of C. • Adaptable to problems by proper selection of kernels.
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
Hessian Hessian Matrix Matrix Substituting αs =  ysΣyi αi into the objective function, Q = αt 1  1/2 αt H α we derive the Hessian matrix. L1 SVM HL1 = (… yi (g(xi) − g(xs) ) …)t(… yi (g(xi) − g(xs) ) …) HL1: positive semidefinite L2 SVM HL2 = HL1 + {(yi yj + δij)/C} HL2: positive definite, which results in stabler training.
Nonunique Nonunique Solutions Solutions Strictly convex functions give unique solutions.
Table Uniqueness Primal Dual
L1 SVM L2 SVM Nonunique* Unique* Nonunique Unique*
*: Burges and Crisp (2000) Convex objective function
Property Property 11 For the L1 SVM, the vectors that satisfy yi(wtg(xi) + b) = 1 are not always support vectors. We call these boundary vectors.
1 3 2
Irreducible Irreducible Set Set of of Support Support Vectors Vectors A set of support vectors is irreducible if deletion of boundary vectors and any support vectors result in the change of the optimal hyperplane.
Property Property 22 For the L1 SVM, let all the support vectors be unbounded. Then the Hessian matrix associated with the irreducible set is positive definite.
Property Property 33 For the L1 SVM, if there is only one irreducible set, and support vectors are all unbounded, the solution is unique.
Irreducible sets {1, 3}, {2, 4}
2 1 4 3
The dual problem is nonunique, but the primal problem is unique.
In general the number of support vectors of L2 SVM is larger than that of L1 SVM.
Computer Computer Simulation Simulation • We used white blood cell data with 13 inputs and 2 classes, each class having app. 400 data for training and testing. • We trained SVMs using the steepest ascent method (Abe et al. (2002)) • We used a personal computer (Athlon 1.6Ghz) for training.
Recognition Recognition Rates Rates for for Polynomial Polynomial Kernels Kernels The difference is small. 100 95 L1 L2 L1 L2
% 90 85 80 75
dot
poly2
poly3
poly4
poly5
Test Test Train. Train.
Support Support Vectors Vectors for for Polynomial Polynomial Kernels Kernels For poly4 and poly5 the numbers are the same. 200 180 160 140 120 100 80 60 40 20 0
L1 L2
dot
poly2
poly3
poly4
poly5
Training Training Time Time Comparison Comparison As the polynomial degree increases, the difference becomes smaller. 60 50 40
(s)
L1 SVM L2 SVM
30 20 10 0
dot
poly2
poly3
poly4
poly5
Summary Summary • The Hessian matrix of an L1 SVM is positive semidefinite, but that of an L2 SVM is always positive definite. • Thus dual solutions of an L1 SVM are nonunique. • When noncritical the difference between L1 and L2 SVMs is small.
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
Multiclass Multiclass SVMs SVMs • Oneagainstall SVMs – Continuous decision functions – Fuzzy SVMs – Decisiontreebased SVMs
• Pairwise SVMs – Decisiontreebased SVMs (DDAGs, ADAGs) – Fuzzy SVMs
• ECOC SVMs • Allatonce SVMs
Oneagainstall Oneagainstall SVMs SVMs Determine the ith decision function Di(x) so that class i is separated from the remaining classes. Classify x into the class with Di(x) > 0. D3(x) = 0 Class 2
Class 2 D2(x) = 0
Class 1
Class 3
Class 1
Class 3
D1(x) = 0
Unclassifiable
Continuous Continuous SVMs SVMs Classify x into the class with max Di(x). 0 > D2(x) > D3(x) 0 > D2(x) > D1(x)
D2(x) > D3(x) D3(x) = 0
Class 2
Class 2
D2(x) = 0
Class 1
Class 3 Class 1 D1(x) = 0
Class 3
Fuzzy Fuzzy SVMs SVMs D1(x) = 1 D1(x) = 0
Class 1
Class 2
Class 3
m11(x)
1 0
Membership function
We define a onedimensional membership function in the direction orthogonal to the decision function Dij(x).
Class Class ii Membership Membership Class i membership function m1(x) = 0.5
mi ( x) = min mij ( x). j =1,..., n
The region that satisfies mi(x) > 0 corresponds to the classifiable region for class i.
m1(x) =1
Class 1
m1(x) = 0
Resulting Resulting Class Class Boundaries Boundaries by by Fuzzy Fuzzy SVMs SVMs The generalization regions are the same with those by continuous SVMs.
Class 2
Class 2
Class 1
Class 3
Class 1
Class 3
Decisiontreebased DecisiontreebasedSVMs SVMs Each node corresponds to the hyperplane. Training step
Classification step
At each node, determine Starting from the top node, calculate the value until a OHP. leaf node is reached. Class i
f i (x ) = 0
f i (x )

+ Class k Classj f j (x ) = 0
f j (x )
Classi + Classj
Class k
The The problem problem of of the the decision decision tree tree • Unclassifiable region can be resolved. • The region of each class depends on the structure of a decision tree. Class i
f i (x ) = 0
Class k
Class i f j(x ) = 0
Class j
Class j f j (x ) = 0
Class k f k (x ) = 0
How do we determine the decision tree?
Determination Determination of of the the decision decision tree tree ?
The overall classification performance becomes worse.
The separable classes need to be separated at the upper node. The separability measures: Euclidean distances between class centers Classification errors by the Mahalanobis distance z z
1 vs. remaining classes Some vs. remaining classes
11 Class Class vs. vs. Remaining Remaining Classes Classes Using Using Distances Distances between between Class Class Centers Centers Class
Separate the farthest class from remaining classes.
i Class
f i (x ) = 0
f l (x ) = 0 Class
j Class
f j (x ) = 0
l
k
Calculate distances between class centers. For each class, find the smallest value. Separate the class with the largest value in step 2. Repeat for remaining classes.
Pairwise Pairwise SVMs SVMs For all pairs of classes i, j, we define n(n1)/2 decision functions and classify x into class arg maxi Di(x) where Di(x) = Σjsign Dij(x) D12 (x) = 0 Class 1 D31(x) = 0
Class 2 Class 3 D23(x) = 0
Unclassifiable regions still exist. D1(x) = D2(x) = D3(x) = 1
Pairwise Pairwise Fuzzy Fuzzy SVMs SVMs The generalization ability of the PFSVM is better than the PSVM.
Class 1
Class 1 class 2
Class 3
PSVM
Class 2 Class 3
PFSVM
Decisiontreebased Decisiontreebased SVMs SVMs (DDAGs) (DDAGs) Generalization regions change according to the tree structure. D12(x) 2
1
D13(x) 3
Class 1
Class 1
D32(x) 1
2
Class 3
3
Class 2
class 2 Class 3
Decisiontreebased Decisiontreebased SVMs SVMs (ADAGs) (ADAGs) For threeclass problems ADAGs and DDAGs are equivalent. In general, DDAGs include ADAGs.
1
2
D12(x)
3
2
1
D13(x) 3
ADAG (Tennis Tournament)
Class 1
D32(x) 1
2
Class 3
3
Class 2
Equivalent DDAG
ECC ECC Capability Capability The maximum number of decision functions = 2n1 1, where n is the number of classes. Error correcting capability = (h1)/2, where h is the Hamming distance. For n = 4, h = 4 and ecc of 1. Class 1 1 1 1 1 1 1 Class 2 1 1 1 1 1 1 Class 3 1 1 1 1 1 1 Class 4 1 1 1 1 1 1
1 1 1 1
Continuous Continuous Hamming Hamming Distance Distance Di(x) = 1
Hamming Distance
Di(x) = 0
= Σ ERi(x) Class k
Class l
1 0
Error function
= Σ(1 − mi(x)) Equivalent to membership functions with sum operators Membership function
Allatonce Allatonce SVMs SVMs Decision functions wi g(x) + bi > wjg(x) + bj for j ≠ i, j=1,…,n Original formulation n ×M variables where n: number of classes M: number of training data Crammer and Singer (2000) ’s M variables
Performance Performance Evaluation Evaluation • Compare recognition rates of test data for oneagainstall and pairwise SVMs. • Data sets used: – – – –
white blood cell data thyroid data hiragana data with 50 inputs hiragana data with 13 inputs 水戸57 水戸 ひ
５９－１６
Japanese License Plate
Data Data Sets Sets Used Used for for Evaluation Evaluation Data
Inputs
Classes
Train.
Test
Blood Cell
13
12
3097
3100
Thyroid
21
3
3772
3428
H50
50
39
4610
4610
H13
13
38
8375
8356
Performance Performance Improvement Improvement for for 1all 1all SVMs SVMs SVM
FSVM
Tree
100 98 96 (%) 94 92 90 88 86 84 82
Performance improves by fuzzy SVMs and decision trees.
Blood
Thyroid
H50
Performance Performance Improvement Improvement for for Pairwise Pairwise Classification Classification SVM ADAG MX ADAV MN
FSVM ADAG Av
100 98
(%) 96
FSVMs are comparable with ADAG MX.
94 92 90 88 86
Blood
Thyroid
H13
Summary Summary • Oneagainstall SVMs with continuous decision functions are equivalent to 1all fuzzy SVMs. • Performance of pairwise fuzzy SVMs is comparable to that of ADAGs with maximum recognition rates. • There is no so much difference between oneagainstall and pairwise SVMs.
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
Research Research Status Status • Too slow to train by quadratic programming for a large number of training data. • Several training methods have been developed: – decomposition technique by Osuna (1997) – KernelAdatron by Friess et al. (1998) – SMO (Sequential Minimum Optimization) by Platt (1999) – Steepest Ascent Training by Abe et al. (2002)
Decomposition Decomposition Technique Technique Decompose the index set into two: B and N. Maximize Q( α) = Σi∈ ∈B αi − C/2 Σi,j∈ ∈B αi αj yi yj H(xi, xj) − C Σi∈ ∈B, j∈ ∈N αi αj yi yj H(xi, xj) − C/2 Σi,j∈ ∈N αi αj yi yj H(xi, xj) + Σi∈ ∈N αi subject to Σ Σi∈ ∈B yi αi = − Σi∈ ∈N yi αi , C ≧ αi ≧0 for i ∈Β fixing αi for i ∈Ν.
Solution Solution Framework Framework
Outer Loop Inner Loop
Use of QP Package e.g., LOQO (1998)
Violation of KKT Cond Solve for working Set {αi  i ∈ B}
Fixed Set {αi  i ∈ N}
αi = 0
Solution Solution without without Using Using QP QP Package Package • SMO – B = 2, i.e., solve the problem for two variables – The subproblem is solvable without matrix calculations
• Steepest Ascent Training – Speedup SMO by solving subproblems with variables more than two
Solution Solution Framework Framework for for Steepest Steepest Ascent Ascent Training Training Outer Loop
Inner Loop Update αi for i ∈ B’ until all αi in B are updated.
Violation of KKT Cond
Working Set {αi  i ∈ B’} B’ ⊇B
Fixed Set {αi  i ∈ N}
αi = 0
Solution Solution Procedure Procedure • Set the index set B. • Select αs and eliminate the equality constraint by substituting αs = − ysΣ yj αj into the objective function. • Calculate corrections by ∂2Q/∂ ∂α2B”)−1 ∂Q/∂ ∂αB” αB” = −(∂ where B” = B’ {s}. • Correct variables if possible.
Calculation Calculation of of corrections corrections • We calculate corrections by the Cholesky decomposition. • For the L2 SVM, since the Hessian matrix is positive definite, it is regular. • For the L1 SVM, since the Hessian matrix is positive semidefinite, it may be singular.
= Dii
If Dii < η, we discard the variables αj, j≧ i.
Update Update of of Variables Variables
1 2 3
• Case 1 Variables are updated. • Case 2 Corrections are reduced to satisfy constraints. • Case 3 Variables are not corrected.
Performance Performance Evaluation Evaluation • Compare training time by the steepest ascent method, SMO, and the interior point method. • Data sets used: – – – –
white blood cell data thyroid data hiragana data with 50 inputs hiragana data with 13 inputs 水戸57 水戸 ひ
５９－１６
Japanese License Plate
Data Data Sets Sets Used Used for for Evaluation Evaluation Data
Inputs
Classes
Train.
Test
Blood Cell
13
12
3097
3100
Thyroid
21
3
3772
3428
H50
50
39
4610
4610
H13
13
38
8375
8356
Effect Effect of of Working Working Set Set Size Size for for Blood Blood Cell Cell Data Data L1 SVM
L2 SVM
1200 1000
(s)
Dot product kernels are used.
800 600
For a larger size, L2 SVMs are trained faster.
400 200 0
2
4
13
20
30
Effect Effect of of Working Working Set Set Size Size for for Blood Blood Cell Cell Data Data (Cond.) (Cond.) L1 SVM
L2 SVM
700 600
Polynomial kernels with degree 4 are used.
500
(s) 400 300
No much difference for L1 and L2 SVMs.
200 100 0
2
20
40
Training Training Time Time Comparison Comparison LOQO
SMO
SAM
10000 1000
LOQO: LOQO is combined with decomposition technique.
(s) 100 10 1
Thyroid Blood H50 H13
SMO is the slowest. LOQO and SAM are comparable for hiragana data.
Summary Summary • The steepest ascent method is faster than SMO and comparable for some cases with the interiorpoint method combined with decomposition technique. • For the critical cases, L2 SVMs are trained faster than L1 SVMs, but for normal cases they are almost the same.
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
SVMinspired SVMinspired Methods Methods • Kernelbased Methods – – – –
Kernel Perceptron Kernel Least Squares Kernel Mahalanobis Distance Kernel Principal Component Analysis
• Maximum Margin Classifiers – Fuzzy Classifiers – Neural Networks
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
Fuzzy Fuzzy Classifier Classifier with with Ellipsoidal Ellipsoidal Regions Regions Membership function mi(x) = exp( hi2(x)) hi2(x) = di2(x)/αi di2(x) = (x – ci)t Qi1 (x – ci) where αi: a tuning parameter, di2(x): a Mahalanobis distance.
Training Training
1. For each class calculate the center and the covariance matrix. 2. Tune the membership function so that misclassification is resolved.
Comparison Comparison of of Fuzzy Fuzzy Classifiers Classifiers with with Support Support Vector Vector Machines Machines • Training of fuzzy classifiers is faster than support vector machines. • Comparable performance for the overlapping classes. • Inferior performance when data are scarce since the covariance matrix Qi becomes singular.
Improvement Improvement of of Generalization Generalization Ability Ability When When Data Data Are Are Scarce Scarce • by Symmetric Cholesky factorization, • by maximizing margins.
Symmetric Symmetric Cholesky Cholesky Factorization Factorization In factorizing Qi into two triangular matrices: Q i = L i L it, if the diagonal element laa is laa < η namely, Qi is singular, we set laa = η.
laa
Concept Concept of of Maximizing Maximizing Margins Margins
If there are no overlap between classes, we set the boundary at the middle of two classes, by tuning αi.
Upper Upper Bound Bound of of ααii The class i datum remains correctly classified for the increase of αi. We calculate the upper bound for all the class i data.
Lower Lower Bound Bound of of ααii The class j datum remains correctly classified for the decrease of αi. We calculate the lower bound for all the data not belonging to class i.
Tuning Tuning Procedure Procedure 1. Calculate the upper bound Li and lower bound Ui of α i. 2. Set αi = (Li + Ui)/2. 3. Iterate the above procedure for all the classes.
Data Data Sets Sets Used Used for for Evaluation Evaluation Data Inputs Classes Train. Test H50
50
39
4610
4610
H105
105
38
8375
8356
H13
13
38
8375
8356
Recognition Rate of Hiragana50 Data (Cholesky Factorization) Test 100 90 80 70 60 (%) 50 40 30 20 10 0
Train.
The recognition rate improved as η was increased.
105 104 103 102 101 η
Recognition Rate of Hiragana50 Data (Maximizing Margins) Test
Train.
100 98
The better recognition rate for small η.
96
(%) 94 92 90 88 86 84
105 104
103 102 101 η
Performance Comparison CF 100 99 98 97 (%) 96 95 94 93 92 91
CF+MM
SVD
SVM
Performance excluding SVD is comparable.
H50
H105
H13
Summary Summary • When the number of training data is small, the generalization ability of the fuzzy classifier with ellipsoidal regions is improved by – the Cholesky factorization with singular avoidance, – tuning membership functions when there is no overlap between classes.
• Simulation results show the improvement of the generalization ability.
Contents Contents 1. Direct and Indirect Decision Functions 2. Architecture of SVMs 3. Characteristics of L1 and L2 SVMs 4. Multiclass SVMs 5. Training Methods 6. SVMinspired Methods 6.1 Kernelbased Methods 6.2 Maximum Margin Fuzzy Classifiers 6.3 Maximum Margin Neural Networks
Maximum Maximum Margin Margin Neural Neural Networks Networks • Training of multilayer neural networks (NNs) • Back propagation algorithm (BP)  Slow training
• Support vector machine (SVM) with sigmoid kernels  High generalization ability by maximizing margins  Restriction to parameter values
• CARVE Algorithm(Young & Downs 1998)  Efficient training method not developed yet
CARVE CARVE Algorithm Algorithm • A constructive method of training NNs • Any pattern classification problems can be synthesized in 3layers (input layer included) • Needs to find hyperplanes that separate data of one class from the others
CARVE CARVE Algorithm Algorithm (hidden (hidden layer) layer) : The class for separation • Only
x2
x1
data are separated
Input space
x1 x2
The weights between input layer and hidden layer represent the hyperplane
Bias Input layer Hidden layer
CARVE CARVE Algorithm Algorithm (hidden (hidden layer) layer) : The class for separation x2
x1
Input space
x1 x2
• Only data are separated • Separated data are not used in next training • When only data of one class remain, the hidden layer training is finished
The weights between input layer and hidden layer represent the hyperplane
Bias Input layer Hidden layer
CARVE CARVE Algorithm Algorithm (output (output layer) layer) (0,1,0) Sigmoid function
(0,0,0)
(0,0,1)
(0,0,1)
1
(1,1,0) (1,0,0)
0
Input space
(0,0,0)
(1,0,0)
Hidden layer output space
All data can be linearly separable by the output layer
Proposed Proposed Method Method • NN training based on CARVE algorithm • Maximizing margins at each layer Optimal hyperplane
• On the positive side, data of other
classes may exist by SVM training
Input space
Not appropriate hyperplanes for CARVE algorithm
Extend DirectSVM method in hidden layer training and use conventional SVM training in output layer
Extension Extension of of DirectSVM DirectSVM • The class labels are set so that the class for separation are +1, and other classes are 1
Nearest data
Largest violation
ex.
target values
: +1 : ‐1
• Determine the initial hyperplane by DirectSVM method Input space
• Check the violation of the data with label –1
Extension Extension of of DirectSVM DirectSVM
Nearest data
• Update the hyperplane so that the misclassified datum with 1 is classified correctly Largest violation
Input space
If there are no violating data with label –1, we stop updating the hyperplane
Training Training of of output output layer layer • Apply conventional SVM training Maximum margin
Set weights by SVM with dot product kernels
x1 x2 Bias Input layer Bias
Hidden layer output space
Hidden layer
Output layer
Data Data Sets Sets Used Used for for Evaluation Evaluation Data
Inputs
Classes
Train.
Test
Blood Cell
13
12
3097
3100
Thyroid
21
3
3772
3428
H50
50
39
4610
4610
H13
13
38
8375
8356
Performance Performance Comparison Comparison BP
FSVM
MMNN
100
FSVM: 1 vs. all
98 96
(%)
MMNN is better than BP and comparable to FSVMs.
94 92 90 88 86
Blood
Thyroid
H50
Summary Summary • NNs are generated layer by layer by the CARVE algorithm and by maximizing margins. • Generalization ability is better than that of BP NN and comparable to that of SVMs.