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Support Vector Machine
YZM 3226 – Makine Öğrenmesi
Outline
◘ Support Vector Machine (Destek Vektör Makineleri)
◘ SVM Tarihçe
◘ Linear Sınıflandırıcılar
◘ SVM Uygulama Alanları
◘ SVM Related Links
Support Vector Machine
◘ Değişkenler arasındaki örüntülerin bilinmediği veri setlerindeki
sınıflama problemleri için önerilmiş bir makine öğrenmesi
yöntemidir»
◘ «Sınıflama, regresyon ve aykırı değer belirleme için kullanılabilen
eğiticili (supervised) öğrenme yöntemidir»
◘ Eğitim verisinde öğrenme yaparak yeni veri üzerinde doğru tahmin
yapmaya ve genelleştirmeye çalışan makine öğrenmesidir»
SVM History
◘ Vapnik and colleagues (1992)—groundwork from Vapnik &
Chervonenkis’ statistical learning theory in 1960s.
V. Vapnik
Support Vector Machines (SVM)
◘ It searches for the linear optimal separating hyperplane (i.e., “decision
boundary”)
◘ SVM finds this hyperplane using support vectors (training tuples) and
margins
Support VectorsSmall Margin Large Margin
SVM—General Philosophy
Support Vectors
Small Margin Large Margin
Support Vector Machines (SVM)
◘ There are infinite lines (hyperplanes)
separating the two classes but we
want to find the best one
(the one that minimizes classification
error on unseen data)
◘ SVM searches for the hyperplane
with the largest margin
October 25, 2016 Data Mining: Concepts and Techniques
SVM—Margins and Support Vectors
SVM—When Data Is Linearly Separable
m
Let data D be (X1, y1), …, (X|D|, y|D|), where Xi is the set of training tuples associated with the class labels yi
There are infinite lines (hyperplanes) separating the two classes but we want to find the best one (the one that minimizes classification error on unseen data)
SVM searches for the hyperplane with the largest margin, i.e., maximum marginal hyperplane (MMH)
Linear Classifiersf x
a
yest
denotes +1
denotes -1
f(x,w,b) = sign(w x + b)
How would you classify this data?
w x + b<0
w x + b>0
Linear Classifiersf x
a
yest
denotes +1
denotes -1
f(x,w,b) = sign(w x + b)
How would you classify this data?
Linear Classifiersf x
a
yest
denotes +1
denotes -1
f(x,w,b) = sign(w x + b)
How would you classify this data?
Linear Classifiersf x
a
yest
denotes +1
denotes -1
f(x,w,b) = sign(w x + b)
Any of these would be fine..
..but which is best?
Linear SVM Mathematically
What we know:
◘ w . x+ + b = +1
◘ w . x- + b = -1
◘ w . (x+-x-) = 2
X-
x+
ww
wxxM
2)(
M=Margin Width
SVM Applications
◘ Used for: classification and numeric prediction
◘ Applications:
– handwritten digit recognition,
– text recognition,
– speech recognition,
– object recognition,
– speaker identification,
– benchmarking time-series prediction tests,
– content-based image retrieval,
– biometrics,
– protein sequence problem
– breast cancer diagnosis
– etc.
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