A study of pattern recognition of Iris flower based on Machine Learning
Yang, Yu (2013)
Turun ammattikorkeakoulu
2013
All rights reserved
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2013102416302
https://urn.fi/URN:NBN:fi:amk-2013102416302
Tiivistelmä
As we all know from the nature, most of creatures have the ability to recognize the objects in order to identify food or danger. Human beings can also recognize the types and application of objects. An interesting phenomenon could be that machines could recognize objects just like us someday in the future. This thesis mainly focuses on machine learning in pattern recognition applications.
Machine learning is the core of Artificial Intelligence (AI) and pattern recognition is also an important branch of AI. In this thesis, the conception of machine learning and machine learning algorithms are introduced. Moreover, a typical and simple machine learning algorithm called K-means is introduced. A case study about Iris classification is introduced to show how the K-means works in pattern recognition.
The aim of the case study is to design and implement a system of pattern recognition for the Iris flower based on Machine Learning. This project shows the workflow of pattern recognition and how to use machine learning approach to achieve this goal. The data set was collected from an open source website of machine learning. The programming language used in this project was Python.
Machine learning is the core of Artificial Intelligence (AI) and pattern recognition is also an important branch of AI. In this thesis, the conception of machine learning and machine learning algorithms are introduced. Moreover, a typical and simple machine learning algorithm called K-means is introduced. A case study about Iris classification is introduced to show how the K-means works in pattern recognition.
The aim of the case study is to design and implement a system of pattern recognition for the Iris flower based on Machine Learning. This project shows the workflow of pattern recognition and how to use machine learning approach to achieve this goal. The data set was collected from an open source website of machine learning. The programming language used in this project was Python.