Machine Learning

Download 30 papers on Machine Learning

25th Jun `14, 10:43 PM in Machine Learning

Are you looking for some serious research papers on Machine Learning? Here you go…! 1. Optimized projections for…

BDMS
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Are you looking for some serious research papers on Machine Learning? Here you go…!

1. Optimized projections for compressed sensing

2. Multiscale sparse image representation with learned dictionaries

3. Robust face recognition via sparse representation

4. Feature selection in face recognition: A sparse representation perspective

5. Random projections for manifold learning

6.  Sparse bayesian learning for basis selection

7. Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization

8. Noise reduction through compressed sensing

9. Using sparse representations for missing data imputation in noise robust speech recognition

10. Noise robust digit recognition using sparse representations

11. Discriminative learned dictionaries for local image analysis

12. Sparse representations for image classification: Learning discriminative and reconstructive non-parametric dictionaries

13. Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain

14. Compressed Least Squares Regression

15. Utilizing Compressibility in Reconstructing Spectrographic Data, with Applications to Noise Robust ASR

16. Learning Sparse Gaussian Markov Networks using a Greedy Coordinate Ascent Approach

17. Online Group-Structured Dictionary Learning

18. Facial Action Unit Recognition with Sparse Representation

19. Collaborative Filtering via Group-Structured Dictionary Learning

20. Automated Word Puzzle Generation via Topic Dictionaries

21. Performance Limits of Dictionary Learning for Sparse Coding

22. Efficient machine learning using random projections

23. Machine Learning in Automated Text Categorization

24. Pattern Recognition and Machine Learning

25. Large-Scale Machine Learning at Twitter

26. Practical Bayesian Optimization of Machine Learning Algorithms

27. Learning Deep Architectures for AI

28. The Computational Complexity of Machine Learning

29. Using Machine Learning To Design And Interpret Gene-Expression Microarrays

30. Reproducing Kernel Banach Spaces for Machine Learning

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