8. The method as set forth in claim 1 , wherein the selecting of categorization labels includes: determining derived probability values for derived categories, each derived category corresponding to an ordered combination of categories from different categorization dimensions, the derived probability value for each derived category being determined based on the probability value of one of the categories of the ordered combination of categories weighted by the probability value of the other one of the categories of the ordered combination; determining an optimized derived category based on the derived probability values; and selecting a categorization label respective to each categorization dimension corresponding to the optimized derived category.
1. A computer-implemented method for categorizing an object respective to at least two categorization dimensions, each categorization dimension being defined by a plurality of categories, the computer-implemented method comprising: determining a probability value indicative of the object for each category of each categorization dimension; and selecting a categorization label for the object respective to each categorization dimension based on (i) the determined probability values of the categories of that categorization dimension and (ii) the determined probability values of categories of at least one other of the at least two categorization dimensions; wherein the at least two categorization dimensions include first and second categorization dimensions, and the selecting of a categorization label respective to each categorization dimension includes: weighting the probability values of the categories of the first categorization dimension based on the probability values of the categories of the second categorization dimension to produce weighted probability values for the categories of the first categorization dimension; weighting the probability values of the categories of the second categorization dimension based on the probability values of the categories of the first categorization dimension to produce weighted probability values for the categories of the second categorization dimension; selecting the label respective to the first categorization dimension based on the weighted probability values for categories of the first categorization dimension; and selecting the label respective to the second categorization dimension based on the weighted probability values for categories of the second categorization dimension.