1. A signal classification system comprising: a computing system executing a training stage configured to receive a plurality of training signals associated respectively with predefined signal classes, each of which comprises a set of signals that have at least one common attribute, receive classification parameters to be used in computing signal parameters so that the predefined signal classes are separated within a vector space, compute, based upon the classification parameters, at least a first instance of a specified signal parameter from at least a first training signal of the training signals, associate the first instance of the specified signal parameter with a first predefined signal class of the predefined signal classes, and store, in a storage element, the first instance of the specified signal parameter as being associated with the first predefined signal class, the first predefined signal class being represented in the storage element by a class representation comprising at least one region to which the first predefined signal class is mapped within the vector space, the region comprising a cell comprising a first N-length vector and thresholds that define at least an edge of the cell within the vector space; and a classifying stage configured to receive an input signal for classification, compute, based upon the classification parameters, at least a second instance of the specified signal parameter from the input signal, the second instance of the specified signal parameter being represented as a second N-length vector, and compare the first instance of the specified signal parameter to the second instance of the specified signal parameter to determine whether to classify the input signal with the first training signal based upon whether the second N-length vector falls within the cell as defined at least in part by the threshold; wherein each of the training signals and the input signal are physical quantities that vary with another quantity in terms of time, space, or both; and wherein the training stage is further configured to create a plurality of sub-cells within the cell that incorporates N-length vectors of at least two of the plurality of training signals, the N-length vectors comprising the first N-length vector of the first training signal, determine whether the cell contains at least a specified number of the N-length vectors, in response to determining that the cell contains at least the specified number of the N-length vectors, replace the cell with the sub-cells, and in response to determining that the cell does not contain at least the specified number of the N-length vectors, retain the cell for further analysis and to eliminate the sub-cells from further analysis.