I want to train a Perceptron using stochastic gradient rulefrom the stream data. I have very limited amount of memory and i can store only $N$ examples.
Now suppose my first $N$ examples come in following fashion and i can classify them correctly as show in the next picture:
and i have classified them as shown. The problem is that since i can't train perceptron for previous $N$ examples(because i can store only $N$ examples and previous $N$ examples need to be thrown away) and training on next $N$ examples contradicts the hyper-plane for previous $N$ examples.
How to train a perceptron from stream data? Do i need to store all examples or there is an alternative way?