๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

[ACM Press the 12th ACM SIGKDD international conference - Philadelphia, PA, USA (2006.08.20-2006.08.23)] Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06 - Supervised probabilistic principal component analysis

โœ Scribed by Yu, Shipeng; Yu, Kai; Tresp, Volker; Kriegel, Hans-Peter; Wu, Mingrui


Book ID
118148761
Publisher
ACM Press
Year
2006
Weight
891 KB
Volume
0
Category
Article
ISBN-13
9781595933393

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


[ACM Press the 12th ACM SIGKDD internati
โœ Yu, Shipeng; Yu, Kai; Tresp, Volker; Kriegel, Hans-Peter; Wu, Mingrui ๐Ÿ“‚ Article ๐Ÿ“… 2006 ๐Ÿ› ACM Press โš– 891 KB

Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g., in a classification or regression task, PCA is however not able to use this information. T

[ACM Press the 12th ACM SIGKDD internati
โœ Carvalho, Vitor R.; Cohen, William W. ๐Ÿ“‚ Article ๐Ÿ“… 2006 ๐Ÿ› ACM Press โš– 725 KB

To learn concepts over massive data streams, it is essential to design inference and learning methods that operate in real time with limited memory. Online learning methods such as perceptron or Winnow are naturally suited to stream processing; however, in practice multiple passes over the same trai