Fast design of reduced complexity nearest-neighbor classifiers using triangular inequality

In this paper, we propose a method of designing a reduced complexity nearest-neighbor (RCNN) classifier with near-minimal computational complexity from a given nearest-neighbor classifier that has high input dimensionality and a large number of class vectors. We applied our method to the classification problem of handwritten numerals in the NIST database. If the complexity of the RCNN classifier is normalized to that of the given classifier, the complexity of the derived classifier is 62 percent, 2 percent higher than that of the optimal classifier. This was found using the exhaustive search.Institute of Information Technology Assessment (IITA), Korea, under research grant 96060-IT2-I2

Analysis of the non-uniform samplings in sigma-delta modul ated signals

The effects of non-uniform sampling in the EA-modulated DAC are discussed in this paper. The non-uniform sampling of the EA-modulated signals causes in-hand noise in the DAC, which will be compared with the harmonic distortions in switched capacitor circuit and the that due to the signal-dependent nonlinearity in the 1-hit DAC. We describe an error-driven edgeselection ulgorithm that suppresses the in-band noise and its implementation. We also propose a model of the in-band noise caused by the non-uniform sampling, which predicts tlhe noise reduction of the spectral shaping in the frequency domain quantitatively based upon the transition probability of the modulator output