kNN Overview Annotated Bibliography

Cover, T., and P. Hart. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory. 13(1): 21-7. This paper contains the conceptual and mathematical basis for the nearest neighbour pattern classification. It was developed from the need to perform analysis for unknown probability densities. Cui, B., J. Shen, G. Cong, H. T. Shen, and Yu, C. 2006. Exploring composite acoustic features for efficient music similarity query. In Proceedings of the 14th Annual ACM international Conference on Multimedia. This paper poses a new index structure, CF tree, for use in companion with kNN to facilitate efficient content-based music search. A new extension to mimic human perception is proposed in MIR context. Feature preprocessing in terms of timbre, rhythm, pitch and DWCHs are well explained. Fujinaga, I. 1996. Exemplar-based learning in adaptive optical music recognition system. In Proceedings of the International Computer Music Conference. 55–6. This paper explains features and characteristics of exemplar-based categorization system with a focus in the k-nearest neighbour classifier. Jiansheng, W. 2009. A novel artificial neural network ensemble model based on k-Nearest Neighbor nonparametric estimation of regression function and its application for rainfall forecasting. In International Joint Conference on Computational Sciences and Optimization. 2:44-8. In this paper, a new artificial neural network model is proposed for use with kNN nonparametric estimation of regression. McKay, C., and I. Fujinaga. 2004. Automatic genre classification using large high-level musical feature sets. In Proceedings of the International Conference on Music Information Retrieval. 525-30. This paper poses a system to extract musical features from MIDI data and uses them to classify recordings by genre. kNN and NN are used to supervise classification. The authors state some characteristics of each system and how them complement their work. Moore. A. 1991. An introductory tutorial on kd-trees. Efficient Memory-based Learning for Robot Control. 6:1-18. This chapter of the Ph. D. Thesis of Andrew Moore gives an specification of the nearest neighbour algorithm and an introduction to the kd-tree data structure Younes, Z., F. Aballah, and T. Denoeux. 2008. Multi-label classification algorithm derived from k-nearest neighbor rule with label dependencies. In Proceedings of the 16th European Signal Processing Conference This explains a new method for multi-label classification problems using kNN. Zhang, H., A. C. Berg, M. Maire, and J. Malik. 2006. SVM-kNN: Discriminative nearest neighbor classification for visual category recognition. In CVPR. 2:2126-36. This paper poses a new method to use SVM and kNN in combination for visual category recognition