Körber-Prize 2019

The decisive trick behind support vector machines is that while the separation of the points takes place cleanly in multidimensional space, the computer does not need to conduct the associated complicated and cumbersome vector calculations. The reason is that the systems work with the scalar products of these vectors, which by definition are numbers and can be easily calculated by the computer. 

»If our sample system continues to make too many mistakes, such as classifying too many females as males and vice versa, the programmer has to make adjustments to the kernel function,« Bauer explains. »The beautiful part of the kernel function is that it is a mathematically precisely defined value and thus a precise setscrew.« In comparison to it, a neural network is an intransparent black box whose parameters—created in a purely statistical manner during the training—lie hidden somewhere in the network weightings.

A humanoid robot named Nao is waiting in the spare parts cabinet 
of the robotics group for researchers to program it.


Support vector machines had their greatest successes during the 1990s. At the Bell Labs, Schölkopf and Vladimir Vapnik together developed a system that could recognize hand-written numbers on letters almost as well as a human—and better than all the competing systems in­cluding neural networks. In 1997, a support vector machine succeeded in automatically evaluating 21,450 news reports from the Reuters News Agency and classi­fying them into 135 categories such as sports, business, and politics.

As a result of their mathematical approach, support vector machines gave computer science and especially its subdiscipline machine learning a significant boost. Bernhard Schölkopf is the most frequently cited German computer scientist and belongs, according to the American research journal Science, to the ten most influential computer scientists in the world. 

»Decisive for the great advances made in machine learn­ing are, above all, the immensely in-creased amounts of data, called big data in the US,« Schölkopf says and explains this by referring to the following example.