Instrumentation & Measurement Magazine 25-9 - 43
method to identify the quality of condiment powders. In this
section, the basic principle of two common ML algorithms including
support vector machine (SVM) and random forest tree
(RFT) were introduced.
Machine Learning Algorithms
ML is a multi-domain interdisciplinary subject, which specializes
in how computers simulate or realize human learning
behaviors to acquire new knowledge and reorganize existing
knowledge structures to continuously improve their performance.
The basic process of ML is to continuously improve the
original model or system based on existing data and experience
knowledge by learning, and then apply the new model or
system to make classification or regression of new data.
Assuming that the input x only has the relationship
with corresponding y, ML could build a nonlinear function
y = f (x, w) with some algorithms by training the model with
observed data pairs (x1
, y1), (x2
, y2), ..., (xn
, yn) where w is the
approximation error. In this research, different ML models
including SVM, decision trees, and random forests will be discussed
and compared to identify the quality of the condiments.
SVM is a supervised learning model, and the algorithm
can be used for small sample classification recognition and regression
analysis. The SVM classifier is able to find the optimal
hyperplane that separates two classes.
The kernel function k(xi
,x)=ϕ(xi
SVM algorithm to solve the support vector hyperplane with a
decision function of:
f Kx x bx,
i
ii
, x)=(xix+1)d
(1)
where α is support vector, K(xi,xj) is kernel function, and b is an
error term. The kernel functions commonly used in nonlinear
Instrumentation & Measurement Magazine 25-9
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