Applied machine learning. ([2019])

• Record Type: Book
• Further Information: Note: David Forsyth.
• Authors: Forsyth, David
• Contents: 1. Learning to Classify -- 2. SVMs and Random Forests -- 3. A Little Learning Theory -- 4. High-dimensional Data -- 5. Principal Component Analysis -- 6. Low Rank Approximations -- 7. Canonical Correlation Analysis -- 8. Clustering -- 9. Clustering using Probability Models -- 10. Regression -- 11. Regression: Choosing and Managing Models -- 12. Boosting -- 13. Hidden Markov Models -- 14. Learning Sequence Models Discriminatively -- 15. Mean Field Inference -- 16. Simple Neural Networks -- 17. Simple Image Classifiers -- 18. Classifying Images and Detecting Objects -- 19. Small Codes for Big Signals -- Index.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 006.3/1; Machine learning; Mechanical engineering
• Languages: English
• ISBNs: 9783030181147; 3030181146
• Notes: Note: Description based on online resource; title from digital title page (viewed on August 23, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.