Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
The covering number in learning theory
Journal of Complexity
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
Consistency of support vector machines and other regularized kernel classifiers
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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In MIMO-OFDM systems, by matching transmitter parameters such as modulation order and coding rate, link adaptation can increase the throughput significantly. However, creating a tractable mathematical mapping model from environmental variables to transmitter parameters that allows the latter to be optimized in any sense, presents serious challenginges due to the large number of variables involved, as well as the complexity required in any model with the ability to accurately capture and explain all factors that affect performance. Machine learning algorithms, which make no mathematical assumptions and use only past observations to model the input-output relationship, have recently been explored for adaptation in MIMO-OFDM systems. In this paper we propose a novel machine learning algorithm based on multi-class support vector machines (SVMs). Our algorithm has considerably smaller operational overhead (including storage requirements) and better performance for link adaptation. With IEEE 802.11n simulations we show that our new algorithm outperforms existing machine-learning based algorithms. Moreover, we show that our algorithm is (asymptotically) consistent, in the sense that as the number of training data used increases, our algorithm obtains the performance-optimal classifier.