Using the characteristics of self-learning, association, and generalization, like the cerebellum of human being, the proposed CMAC-based pattern recognition scheme enables a powerful, straightforward, and efficient pattern recognition method.
We have modeled the theoretical response function of the array when sensing a mixture of gases, based on data from these commercial sensors, and designed an appropriate pattern recognition scheme for the simultaneous identification of a given gas and its concentration in the mixture.
In addition, it has become obvious that the performance reported by the different feature selection strategies depends largely on the pattern recognition scheme implemented to classify the data.
Moreover, wavelet entropy measures have been introduced for the implementation of pattern recognition schemes and seem to provide high performances in diagnosing cardiac diseases [21].
The pattern recognition schemes were evaluated using two different procedures: The calculation of several performance measures such as sensitivity (Se), specificity (Sp), positive predictive value (Pp), negative predictive value (Np) and accuracy (Ac), all in the interval [0.00,100.00].
In this work, HRV analysis methods and pattern recognition schemes (namely, artificial neural networks and support vector machines) were used to discriminate between healthy control subjects and cardiovascular risk patients.
We strived to maximize inclusion of relevant attributes to mitigate the problem of overfitting to increase model replicability [ 96], while excluding unimportant attributes that may be detrimental to pattern recognition schemes performance.
We report the development of a pattern-recognition scheme for the off-lattice self-learning kinetic Monte Carlo (KMC) method, one that is simple and flexible enough that it can be applied to all types of surfaces.
The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function.
The two examination formats will be tested for their ability to elicit the three different diagnostic reasoning strategies generally available to learners: hypothetico-deductive reasoning, pattern recognition, and scheme-inductive reasoning.
We analyse a recent image authentication scheme designed by Chang et al. [A watermarking-based image ownership and tampering authentication scheme, Pattern Recognition Lett. 27 (5) (2006) 439–446] whose first step is based on a watermarking scheme of Maniccam and Bourbakis [Lossless compression and information hiding in images, Pattern Recognition 37 (3) (2004) 475 486].
