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KDD
2004
ACM
166views Data Mining» more  KDD 2004»
14 years 9 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
ISMIR
2004
Springer
118views Music» more  ISMIR 2004»
14 years 2 months ago
Learning to Align Polyphonic Music
We describe an efficient learning algorithm for aligning a symbolic representation of a musical piece with its acoustic counterpart. Our method employs a supervised learning appr...
Shai Shalev-Shwartz, Joseph Keshet, Yoram Singer
BMCBI
2007
178views more  BMCBI 2007»
13 years 8 months ago
SVM clustering
Background: Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being cons...
Stephen Winters-Hilt, Sam Merat
ICRA
2010
IEEE
164views Robotics» more  ICRA 2010»
13 years 7 months ago
Boundary detection based on supervised learning
— Detecting the boundaries of objects is a key step in separating foreground objects from the background, which is useful for robotics and computer vision applications, such as o...
Kiho Kwak, Daniel F. Huber, Jeongsook Chae, Takeo ...
CVPR
2003
IEEE
14 years 10 months ago
Classification Based on Symmetric Maximized Minimal Distance in Subspace (SMMS)
We introduce a new classification algorithm based on the concept of Symmetric Maximized Minimal distance in Subspace (SMMS). Given the training data of authentic samples and impos...
Wende Zhang, Tsuhan Chen