This paper presents novel architectures for machine learning based classifiers using stochastic logic. Two types of classifier architectures are presented. These include: linear support vector machine (SVM) and artificial neural network (ANN). Stochastic computing systems require fewer logic gates and are inherently fault-tolerant. Thus, these structures are well suited for nanoscale CMOS technologies. These architectures are validated using seizure prediction from electroencephalogram (EEG) as an application example. To improve the accuracy of proposed stochastic classifiers, a novel approach based on linear transformation of input data is proposed for EEG signal classification using linear SVM classifiers. Simulation results in terms of the classification accuracy are presented for the proposed stochastic computing and the traditional binary implementations based datasets from one patient. Compared to conventional binary implementation, the accuracy of the proposed stochastic ANN is improved by 5.89%. Synthesis results are also presented for EEG signal classification. Compared to the traditional binary linear SVM, the hardware complexity, power consumption and critical path of the stochastic implementation are reduced by 78%, 74% and 53%, respectively. The hardware complexity, power consumption and critical path of the stochastic ANN classifier are reduced by 92%, 88% and 47%, respectively, compared to the conventional binary implementation.