The nature of statistical learning theory
The nature of statistical learning theory
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An empirical evidence of winner's curse in electronic auctions
ICIS '99 Proceedings of the 20th international conference on Information Systems
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Price prediction and insurance for online auctions
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Expert Systems with Applications: An International Journal
A support system for predicting eBay end prices
Decision Support Systems
Price prediction in a trading agent competition
Journal of Artificial Intelligence Research
The effects of shilling on final bid prices in online auctions
Electronic Commerce Research and Applications
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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We propose a systematic procedure for assessing the end price of an item in a C-to-C auction site. These sites deal with used product and the product features vary substantially even within a single product category that makes price assessment difficult. Besides, the true market demand at a particular time, the effect of spurious bidding activities also contributes to price variation. We suggest removing outliers, selecting the right features and clustering the product data can increase the prediction accuracy. Using a multivariate dataset from eBay on Dell Laptops, we show, the prediction accuracy using back propagation neural network improves considerably when used in combination with the methods for (1) removing outliers using Support Vector Data Description, (2) selecting the right features using mutual information as a measure, and (3) clustering of the datasets.