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ICCV
2005
IEEE
16 years 1 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
SCESM
2006
ACM
269views Algorithms» more  SCESM 2006»
15 years 5 months ago
Inferring operational requirements from scenarios and goal models using inductive learning
Goal orientation is an increasingly recognised Requirements Engineering paradigm. However, integration of goal modelling with operational models remains an open area for which the...
Dalal Alrajeh, Alessandra Russo, Sebastián ...
94
Voted
UIST
1997
ACM
15 years 4 months ago
Simplifying Component Development in an Integrated Groupware Environment
This paper describes our experiences implementing a component architecture for TeamWave Workplace, an integrated groupware environment using a rooms metaphor. The problem we faced...
Mark Roseman, Saul Greenberg
CVPR
2007
IEEE
16 years 1 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
ICCV
2005
IEEE
15 years 5 months ago
Learning Models for Predicting Recognition Performance
This paper addresses one of the fundamental problems encountered in performance prediction for object recognition. In particular we address the problems related to estimation of s...
Rong Wang, Bir Bhanu