Proceedings Article | 9 April 2007
KEYWORDS: Neurons, Pattern recognition, Brain mapping, Image classification, Evolutionary algorithms, Detection and tracking algorithms, Neural networks, Computing systems, Algorithms, Error analysis
Various kinds of classification methods have been developed. However, most of these classical methods, such as
Back-Propagation (BP), Bayesian method, Support Vector Machine(SVM), Self-Organizing Map (SOM) are arrogant.
A so-called arrogance, for a human, means that his decision, which even is a mistake, overstates his actual experience.
Accordingly, we say that he is a arrogant if he frequently makes arrogant decisions. Likewise, some classical pattern classifiers
represent the similar characteristic of arrogance. Given an input feature vector, we say a classifier is arrogant in its classification if its
veracity is high yet its experience is low. Typically, for a new sample which is distinguishable from original training samples,
traditional classifiers recognize it as one of the known targets. Clearly, arrogance in classification is an undesirable attribute.
Conversely, a classifier is non-arrogant in its classification if there is a reasonable balance between its veracity and its
experience. Inquisitiveness is, in many ways, the opposite of arrogance. In nature, inquisitiveness is an eagerness for knowledge
characterized by the drive to question, to seek a deeper understanding. The human capacity to doubt present beliefs allows us to
acquire new experiences and to learn from our mistakes. Within the discrete world of computers, inquisitive pattern recognition is the
constructive investigation and exploitation of conflict in information. Thus, we quantify this balance and discuss new techniques that
will detect arrogance in a classifier.
