Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Window training, based on an extended form of stochastic approximation, offers a means of producing linear classifiers that minimize the probability of misclassification of statistically generated data. Associated with window training is a window criterion function.  show that minimizing the window criterion function yields a linear classifier that minimizes the probability of misclassification (i.e., the "error rate"). Hover window training may produce a local minimum that exceeds the global minimum error rate.  show that this defect does not occur in the error-correcting perceptron. The criterion minimized by that training procedure is "convex"; i.e., the perceptron criterion has only one local minimum. Consequently  recommend that window training be preceded by perceptron training, the perceptron training producing a decision surface which the window training process will move to a position that is likely to be globally optimum. >
['Leon Bobrowski', 'Jack Sklansky']
I am writing a research paper on a idea Linear classifiers by window training
[{"from": "human", "value": "I am writing a research paper on a idea Linear classifiers by window training"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Window training, based on an extended form of stochastic approximation, offers a means of producing linear classifiers that minimize the probability of misclassification of statistically generated data. Associated with window training is a window criterion function.  show that minimizing the window criterion function yields a linear classifier that minimizes the probability of misclassification (i.e., the \"error rate\"). Hover window training may produce a local minimum that exceeds the global minimum error rate.  show that this defect does not occur in the error-correcting perceptron. The criterion minimized by that training procedure is \"convex\"; i.e., the perceptron criterion has only one local minimum. Consequently  recommend that window training be preceded by perceptron training, the perceptron training producing a decision surface which the window training process will move to a position that is likely to be globally optimum. >"}]