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December 2004

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November Editorial 2004

Classification with Support Vector Machines

Classification is very common task in chemometrics and several methods are reported in the literature for solving this type of problems. The choice of the most appropriate method depends on the specific nature of the problem, for example how many classes, objects and variables describe the dataset and so on. In simpler cases (e.g. linear boundaries, many samples and fewer variables) many algorithms may perform satisfactorily, whereas for most complicated cases the choice for reaching optimal performance is usually more restricted.
Support Vector Machines (SVMs) are a further approach for classification that has been introduced only recently by researchers from the machine learning community. They have attracted much attention due to some particular features amongst which their flexibility to deal with tasks of diverse complexity, their robustness and computational efficiency when dealing with many variables and their clear connection to statistical learning theory. In this article we introduce the main features of SVMs by means of simple numerical examples.


Simeone Zomer

Centre for Chemometrics
University of Bristol
United Kingdom

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First circular: SSC9, Reykjavik, Iceland
22-25 August 2005

The 9th Scandinavian Symposium on Chemometrics (SSC9) will take place in Reykjavik, Iceland on August 21-25 2005.

First circular can be downloaded (pdf)

Chair and co-chair are Agnar Høskuldsson and Margrét Þorsteinsdóttir.The conference website is http://www.conference.is/ssc9/.

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Homepage of Chemometrics website reaches new record levels in the number of visits with an increase of almost 700% compared to 1999 (see Figure below). Many thanks to all of you who have contributed and made it the most popular non-commercial chemometrics website with one goal only, to spread the word of chemometrics and make it more available to the general scientific community.
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