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» Experimental perspectives on learning from imbalanced data
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ML
1998
ACM
153views Machine Learning» more  ML 1998»
14 years 9 months ago
Bayesian Landmark Learning for Mobile Robot Localization
To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optim...
Sebastian Thrun
ECCV
2002
Springer
15 years 11 months ago
Probabilistic Search for Object Segmentation and Recognition
Abstract. The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range ...
Ulrich Hillenbrand, Gerd Hirzinger
ENC
2006
IEEE
15 years 3 months ago
Cleaning Training-Datasets with Noise-Aware Algorithms
We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discri...
H. Jair Escalante
NPL
2006
85views more  NPL 2006»
14 years 9 months ago
A Neural Model for Context-dependent Sequence Learning
A novel neural network model is described that implements context-dependent learning of complex sequences. The model utilises leaky integrate-and-fire neurons to extract timing inf...
Luc Berthouze, Adriaan G. Tijsseling
ICDM
2007
IEEE
124views Data Mining» more  ICDM 2007»
15 years 4 months ago
Community Learning by Graph Approximation
Learning communities from a graph is an important problem in many domains. Different types of communities can be generalized as link-pattern based communities. In this paper, we p...
Bo Long, Xiaoyun Xu, Zhongfei (Mark) Zhang, Philip...