The Support Vector Machine is established as one of the leading approaches to pattern recognition and machine learning worldwide. In addition to their application in molecular diagnostics, SVMs are replacing neural networks in a variety of fields including engineering, information retrieval, and bioinformatics. They have received widespread notice for their ability to discover hidden relationships in datasets and have been exploited for numerous applications, including homeland security techniques, text and image classification, handwriting and speech recognition, fingerprint identification, face recognition, fraud detection and security systems, financial securities and futures prediction, image analysis, protein structure prediction, medical diagnosis and prognosis, classification of microarray gene expression data, identifying consumer purchasing patterns, personal recommendation systems, weather prediction, decryption, engine knock detection and vehicle control and monitoring, prediction of traffic variables in transportation systems, and many others.
Many commercial institutions have already incorporated SVMs into product and research applications. Industries utilizing SVM technology include large corporations within the software and computer fields, biomedical and pharmaceutical companies, and automotive, financial, and retail industries. Educational and research institutions throughout the world have successfully applied SVMs to a wide array of applications including gene and protein expression analysis, medical image analysis and mass spectrometry.