Statistical Pattern Recognition

Pattern Analysis and Applications -     

Adaptive Pattern Recognition

A long term goal of research in artificial intelligence is todetermine and to implement principles which permit a movable machine to direct its actions depending uponsensory feed-back from its environment. This paper concentrates onspatial sensors which input images (2-dimensional arrays). A proposal is put forward in which the machine adaptsto the actual data, and examplesare given of input prediction, of detection of unexpected events, andof recognition of spatial patterns.The image sequence is locally partitioned into temporally contiguous subsequences of afixed spatial extent. The spatial extent is constant over time and the temporal extent of a subsequence is maximizedsubject to the condition that the subsequence has occurred previously. The principle is illustrated on image sequences. It is further demonstrated on images which are structured as pseudo-temporal sequences of their rows. The demonstrations use diverse complex and simple examples to illustrate theversatility of the method. The demonstrations show that to a large degree it is not necessary for the user to supply explicit models for different patternrecognition tasks.     

Quantum Pattern Recognition

I review and expand the model of quantum associative memory that I have recently proposed. In this model binary patterns of n bits are stored in the quantum superposition of the appropriate subset of the computational basis of n qbits. Information can be retrieved by performing an input-dependent rotation of the memory quantum state within this subset and measuring the resulting state. The amplitudes of this rotated memory state are peaked on those stored patterns which are closest in Hamming distance to the input, resulting in a high probability of measuring a memory pattern very similar to it. The accuracy of pattern recall can be tuned by adjusting a parameter playing the role of an effective temperature. This model solves the well-known capacity shortage problem of classical associative memories, providing a large improvement in capacity. PACS: 03.67.-a     

Pattern Recognition in Micro-Economics

A property which is intrinsic to the field of economics is the idea of aggregation. From the concept of a utility function which arose through the combining of wants or needs to input-output studies the concept of aggregation plays an important part in the study of economic systems-both micro and macro. Even though aggregation plays an important role in economics its explicit use is limited to the manipulation of statistical data. The technique of pattern recognition also involves the problem of aggregation. In fact pattern recognition is aggregation. This is quite obvious when we see that in pattern recognition processes large quantities of data are classified as belonging to various predetermined pattern classes. This paper will deal with aggregation in a particular area of consumer choice by using an elementary idea of pattern reognition The particular problem that we shall be dealing with is that of transitivity in individual decision procedures. We shall approach this particular problem through the idea of a generalized characteristic function of a set–a fuzzy set.