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17 years 3 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
ICDM
2009
IEEE
155views Data Mining» more  ICDM 2009»
15 years 11 months ago
Stacked Gaussian Process Learning
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
ICDAR
2009
IEEE
15 years 11 months ago
Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation
This paper proposes a recognition based approach to handwritten numeral string segmentation. We consider two classes: numeral strings segmented correctly or not. The feature vecto...
Yanjie Wang, Xiabi Liu, Yunde Jia
NIPS
2004
15 years 6 months ago
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection
In this paper we propose a probabilistic model for online document clustering. We use non-parametric Dirichlet process prior to model the growing number of clusters, and use a pri...
Jian Zhang 0003, Zoubin Ghahramani, Yiming Yang
ICCV
2009
IEEE
16 years 9 months ago
Evaluating Information Contributions of Bottom-up and Top-down Processes
This paper presents a method to quantitatively evaluate information contributions of individual bottom-up and topdown computing processes in object recognition. Our objective is...
Xiong Yang, Tianfu Wu, Song-Chun Zhu