💬 AI Algorithms
1. Artificial Intelligence
At a conference held at Dartmouth in 1956, a group of 10 scientists established the field of artificial intelligence and first introduced the term 'artificial intelligence'.
1-1. machine learning
It performs
functions through field classification and clustering to develop algorithms and
technologies that allow computers to learn with
Supervised
Learning: Guess the value you want to predict from the training data
Self-learning:
Computers learn data on their own without people.
Reinforcement
Learning: In a given environment, the agent recognizes the current situation
and takes an action, and receives positive and negative rewards accordingly.
1-2. artificial neural network
An
algorithm that has problem-solving ability by changing the strength of synaptic
bonding through learning in which artificial neurons that form a network
through synaptic bonding
1-3. deep learning
Stacking
various kinds of artificial neural networks 'complex' and 'very deep'
Among the
algorithms related to artificial intelligence, the most common and widely used
is pattern recognition.
2. Pattern Recognition
2-1. pattern recognition
What should
be preceded in artificial intelligence: recognizing patterns for given data
usage
example:
Ø Recognizing
license plates at highway tollgates
Ø Diagnose a
patient from complex test results
Ø Automatically
correct spelling when typing on keyboard on your phone
Ø Decide which
ads to show to specific users on your portal
Algorithm
Used:
Ø Neighbor
Classifier Algorithm
Ø Decision Tree
Algorithm
Ø Artificial
Neural Network Algorithm
2-2. Nearest Neighbor Classifier Algorithm
·
Algorithm for recognizing letters and numbers
written by human hands or on car license plates
·
Accuracy averages 97%.
Principle:
1-Each of
the various shapes of a specific number or character is stored in units of
pixels.
At this
time, the more various shapes are stored, the higher the accuracy.
2-Enter a
specific number or letter
3-Each
pixel of the received number and one stored data are selected and compared in
units of pixels.
4-If the
comparison result is the same, set each pixel to white, otherwise set each
pixel to black.
5-Finally,
if the same thing (white) is 94%, it is judged to be a correct number, and if
the same thing is 70% or less, it is judged not to match the input number.
2-3. kNearest Neighbor Classification Algorithm
Analysis of
where a particular voter will vote in an election.
&
frequently used in various marketing analysis
Comments