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» Evaluating learning algorithms and classifiers
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ECCV
2004
Springer
15 years 10 months ago
Statistical Learning of Evaluation Function for ASM/AAM Image Alignment
Alignment between the input and target objects has great impact on the performance of image analysis and recognition system, such as those for medical image and face recognition. A...
Xiangsheng Huang, Stan Z. Li, Yangsheng Wang
PR
2008
85views more  PR 2008»
15 years 4 months ago
Quadratic boosting
This paper presents a strategy to improve the AdaBoost algorithm with a quadratic combination of base classifiers. We observe that learning this combination is necessary to get be...
Thang V. Pham, Arnold W. M. Smeulders
145
Voted
KDD
2004
ACM
166views Data Mining» more  KDD 2004»
16 years 5 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
SIGIR
2012
ACM
13 years 7 months ago
Top-k learning to rank: labeling, ranking and evaluation
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Shuzi Niu, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
ECCV
2004
Springer
16 years 6 months ago
Learning Outdoor Color Classification from Just One Training Image
We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training im...
Roberto Manduchi