1 a statistical mechanical analysis of online learning: seiji miyoshi kobe city college of...
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A Statistical Mechanical Analysis of Online Learning:
Seiji MIYOSHIKobe City College of Technology
miyoshi@kobe-kosen.ac.jp
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Background (1)
• Batch Learning– Examples are used repeatedly– Correct answers for all examples– Long time– Large memory
• Online Learning– Examples used once are discarded– Cannot give correct answers for all examples– Large memory isn't necessary– Time variant teacher
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A Statistical Mechanical Analysis of Online Learning:
Can Student be more Clever than Teacher ?
Seiji MIYOSHIKobe City College of Technology
miyoshi@kobe-kosen.ac.jp
Jan. 2006
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BMoving Teacher
JStudent
True Teacher
A
Jan. 2006
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A Statistical Mechanical Analysis of Online Learning:
Seiji MIYOSHIKobe City College of Technology
miyoshi@kobe-kosen.ac.jp
Many Teachers or Few Teachers ?
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B B
B B
k'k
K 1A
J
True teacher
Student
Ensemble teachers
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P U R P O S EP U R P O S ETo analyze generalization performance of a model composed of a student, a true teacher and K teachers (ensemble teachers) who exist around the true teacher
To discuss the relationship between the number, the diversity of ensemble teachers and the generalization error
B B
B B
k'k
K 1A
J
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M O D E L (1/4)M O D E L (1/4)True teacher
Student
• J learns B1,B2, ・・・ in turn.
• J can not learn A directly.
• A, B1,B2, ・・・ ,J are linear perceptrons with noises.
Ensemble teachers
B B
B B
k'k
K 1A
J
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Simple Perceptron
J1
x1 xN
JN
Output
Inputs
Connection weights
)sgn(Output1
N
iiixJ
+1
-1
10
J1
x1 xN
JN
Output
Inputs
Connection weights
)sgn(Output1
N
iiixJ
Simple Perceptron
N
iiixJ
1
Output
Linear Perceptron
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B
BB
B 1
kk'
KA
J
M O D E L (2/4)M O D E L (2/4)
Linear Perceptrons with Noises
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M O D E L (3/4)M O D E L (3/4)• Inputs: • Initial value of student:
• True teacher: • Ensemble teachers:
• N→∞ (Thermodynamic limit)
• Order parameters– Length of student– Direction cosines
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B B
B B
R
q
R
RJ
B k
k'k
K 1
BkJ
kk'
A
J
True teacher
Student
Ensemble teachers
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fkm
Student learns K ensemble teachers in turn.
M O D E L (4/4)M O D E L (4/4)
Gradient method
Squared errors
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GENERALIZATION ERRORGENERALIZATION ERROR• A goal of statistical learning theory is to obtain generalization error theoretically.
• Generalization error = mean of errors over the distribution of new input
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Simultaneous differential equations in deterministic forms, Simultaneous differential equations in deterministic forms, which describe dynamical behaviors of order parameterswhich describe dynamical behaviors of order parameters
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Analytical solutions of order parametersAnalytical solutions of order parameters
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GENERALIZATION ERRORGENERALIZATION ERROR• A goal of statistical learning theory is to obtain generalization error theoretically.
• Generalization error = mean of errors over the distribution of new input
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Dynamical behaviors of generalization error, Dynamical behaviors of generalization error, RRJJ and and ll
（ η=0.3, K=3, RB=0.7, σA2=0.0, σB
2=0.1, σJ2=0.2 ）
Ord
er P
ara
me
ters
t=m/N
q=1.00
l
R
q=0.80q=0.60q=0.49
0 5
0.2
0.4
0.6
0.0
1.0
0.8
10 15 20
Student
Ensembleteachers
Ge
ner
ali
zati
on
Err
or
t=m/N
q=1.00q=0.80q=0.60q=0.49
0 50.2
0.4
0.6
1.2
1.0
0.8
10 15 20
J
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Analytical solutions of order parametersAnalytical solutions of order parameters
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Steady state analysisSteady state analysis （ （ tt → → ∞ ∞ ））
・ If η ＜０ or η＞２
・ If ０＜ η ＜２
Generalization error and length of student diverge.
If η ＜１ , the more teachers exist or the richer the diversity of teachers is, the cleverer the student can become.
If η ＞１ , the fewer teachers exist or the poorer the diversity of teachers is, the cleverer the student can become.
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Steady value of generalization error, Steady value of generalization error, RRJJ and and ll
（ K=3, RB=0.7, σA2=0.0, σB
2=0.1, σJ2=0.2 ）
0 0.5 1 1.5 2
q=1.00q=0.80q=0.60q=0.49
η
0.2
0.4R
0.6
0.0
0.8
Ge
ner
ali
zati
on
Err
or
00.1
1
10
0.5 1 1.5 2
q=1.00q=0.80q=0.60q=0.49
ηJ
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Steady value of generalization error, Steady value of generalization error, RRJJ and and ll
（ q=0.49, RB=0.7, σA2=0.0, σB
2=0.1, σJ2=0.2 ）
0 0.5 1 1.5 2
η
K=1K=3K=10K=30
0.2
0.4R
0.6
0.0
0.8
1.0
Ge
ner
ali
zati
on
Err
or
0.1
1
10
0 0.5 1 1.5 2
η
K=1K=3K=10K=30
J
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CONCLUSIONSCONCLUSIONSWe have analyzed the generalization performance of a student in a model composed of linear perceptrons: a true teacher, K teachers, and the student.
Calculating the generalization error of the student analytically using statistical mechanics in the framework of on-line learning, we have proven that when the learning rate satisfies η<1, the larger the number K is and the more diversity the teachers have, the smaller the generalization error is. On the other hand, when η>1, the properties are completely reversed.
If the diversity of the K teachers is rich enough, the direction cosine between the true teacher and the student becomes unity in the limit of η→0 and K→∞.
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