We offer a critique of Ishai's [Ishai, A., 2008. Let's face it: it's a cortical network. NeuroImage. doi:10.1016/j.neuroimage.2007.10.040] comment on the value of considering the brain areas that support face perception as a network. We emphasise that this idea is not in opposition to the notion that the fusiform gyrus plays a key role in the visual analysis of faces. More important, we argue t...

Compared to binary logic, the multi-valued logic circuits provide very small chip size, higher speed, and small number of interconnections and simpler realization of logic functions. However, the main drawback of these circuits is the lower noise margin. The noise margin decreases as the radix of multi-valued system increases. Due to the low noise margins, it is necessary to restore or recover ...

We generalize Sauer’s Lemma to finite V C-dimension classes H of binary-valued functions on [n] = {1, . . . , n} which have a margin of at least N on every element in a sample S ⊆ [n] of cardinality l, where the margin μh(x) of h ∈ H on a point x ∈ [n] is defined as the largest non-negative integer a such that h is constant on the interval Ia(x) = [x− a, x+ a].

The nearest neighbor classification is a simple and yet effective technique for pattern recognition. Performance of this technique depends significantly on the distance function used to compute similarity between examples. Some techniques were developed to learn weights of features for changing the distance structure of samples in nearest neighbor classification. In this paper, we propose an ap...

The DC open-loop voltage Gain is a very important specification for amplifiers. In order to increase this specification, in this paper, we have proposed an improved folded cascode gain boosted amplifier (improved FCGBA) which has extremely large DC open-loop voltage gain. Compared to traditional FCGBA, this novel structure needs only one auxiliary amplifier, so it is energy saving. Finally, wit...

We define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers. In the probabilistic framework, we obtain classifiers that optimize the cross-entropy function. In the possibilistic framework, we obtain classifiers that maximize the interclass margin. Furthermore, we show ...