Bayesian model combination (BMC) is a modification to the Bayesian model averaging (BMA) model. BMC ensembles from the space of possible models. BMC provides us with great results. The performance is far better than the bagging and BMA algorithm. BMC uses cross validation methods to approximate results from the model, thus helping us select better ensembles from a collection of ensembles.
Bayesian model combination
by Sinan Ozdemir, Soma Halder
Hands-On Machine Learning for Cybersecurity
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Basics of Machine Learning in Cybersecurity
- What is machine learning?
- Problems that machine learning solves
- Why use machine learning in cybersecurity?
- Current cybersecurity solutions
- Data in machine learning
- Different types of machine learning algorithm
- Algorithms in machine learning
- The machine learning architecture
- Hands-on machine learning
- Summary
- What is machine learning?
- Time Series Analysis and Ensemble Modeling
- Segregating Legitimate and Lousy URLs
- Knocking Down CAPTCHAs
- Using Data Science to Catch Email Fraud and Spam
- Efficient Network Anomaly Detection Using k-means
- Decision Tree and Context-Based Malicious Event Detection
- Catching Impersonators and Hackers Red Handed
- Changing the Game with TensorFlow
- Financial Fraud and How Deep Learning Can Mitigate It
- Case Studies
- Other Books You May Enjoy
Bayesian model combination
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