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LVQ can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all Hebbian learning-based approach.It is a precursor to self-organizing maps (SOM) and related to neural gas and the k-nearest neighbor algorithm (k-NN).
Each cash inflow/outflow is discounted back to its present value (PV). Then all are summed such that NPV is the sum of all terms: = (+) where: t is the time of the cash flow; i is the discount rate, i.e. the return that could be earned per unit of time on an investment with similar risk