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» Predicting relative performance of classifiers from samples
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ECAI
2004
Springer
14 years 1 months ago
Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets
Advances in data collection technologies allow accumulation of large and high dimensional datasets and provide opportunities for learning high quality classification and regression...
Kang Peng, Zoran Obradovic, Slobodan Vucetic
NIPS
2004
13 years 9 months ago
Object Classification from a Single Example Utilizing Class Relevance Metrics
We describe a framework for learning an object classifier from a single example. This goal is achieved by emphasizing the relevant dimensions for classification using available ex...
Michael Fink 0002
CSB
2005
IEEE
124views Bioinformatics» more  CSB 2005»
14 years 1 months ago
Multi-Scale Hierarchical Structure Prediction of Helical Transmembrane Proteins
As the first step toward a multi-scale, hierarchical computational approach for membrane protein structure prediction, the packing of transmembrane helices was modeled at the resi...
Zhong Chen, Ying Xu
ICMLA
2008
13 years 9 months ago
Highly Scalable SVM Modeling with Random Granulation for Spam Sender Detection
Spam sender detection based on email subject data is a complex large-scale text mining task. The dataset consists of email subject lines and the corresponding IP address of the em...
Yuchun Tang, Yuanchen He, Sven Krasser
CORR
2007
Springer
112views Education» more  CORR 2007»
13 years 7 months ago
Learning from compressed observations
— The problem of statistical learning is to construct a predictor of a random variable Y as a function of a related random variable X on the basis of an i.i.d. training sample fr...
Maxim Raginsky