For my research I am required to develop a computational model that incorporates various parameters( with various measurement units) to be the bases of visual task classifier.
I read some papers, where some authors used Bayesian inference and others used Markov Model.
I understand general concept of both techniques, but I would like to understand them in a better way.
Can anyone kindly suggest easy to understand resources that explain how such techniques are used in developing mathematical models and in classification?
Hi! If you are not familiar with mathematical modeling, there are three resources I would recommend above all:
Bishop - Pattern recognition and machine learning (a real classic in Bayesian machine learning)
Hastie, Tibshirani, Friedman - The elements of statistical learning (a frequentist view on learning)
Murphy - Machine learning (a modern book with a lot of applications)
https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?ie=UTF8&qid=1468576285&sr=8-1&keywords=bishop+pattern
https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ref=sr_1_1?ie=UTF8&qid=1468576396&sr=8-1&keywords=murphy+machine
https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=sr_1_1?ie=UTF8&qid=1468576342&sr=8-1&keywords=hastie+tibshirani
- Badges
- Science topic
- Similar topics
- Linguistics
- Composition Studies
- Publication