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This work reports the results obtained with the application of HighOrder Boltzmann Machines without hidden units to constructclassifiers for some problems that represent different learningparadigms. The Boltzmann Machine weight updating algorithm remainsthe same even when some of the units can take values in a discreteset or in a continuous interval. The absence of hidden units and therestriction to classification problems allows for the estimation ofthe connection statistics, without the computational cost involved inthe application of simulated annealing. In this setting, the learningprocess can be sped up several orders of magnitude with noappreciable loss of quality of the results obtained.