Hi all, I find the description of the perceptron classifier a little bit ambiguous and was wondering if it would be worthwhile to clarify it a little bit. What do you think?
While browsing through the documentation at http://scikit-learn.org/stable/modules/linear_model.html I found the following paragraph about perceptrons: > The Perceptron is another simple algorithm suitable for large scale learning. > By default: > • It does not require a learning rate. > • It is not regularized (penalized). > • It updates its model only on mistakes. > The last characteristic implies that the Perceptron is slightly faster to > train than SGD with the hinge loss and that the resulting models are sparser. To me, it sounds like the "classic" Rosenblatt Perceptron update rule weights = weights + eta(yi - yi_pred)xi where yi_pred = sign(w^T.x) [yi_pred ele in {-1, 1 }] However, when I read the documentation on http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron > Perceptron and SGDClassifier share the same underlying implementation. In > fact, Perceptron() is equivalent to SGDClassifier(loss=”perceptron”, eta0=1, > learning_rate=”constant”, penalty=None). it sounds more like the slightly more modern online learning variant of gradient descent (i.e. stochastic gradient descent): weights = weights + eta(yi - yi_pred)xi where yi_pred = w^T.x [yi_pred ele Real] Best, Sebastian ------------------------------------------------------------------------------ Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server from Actuate! Instantly Supercharge Your Business Reports and Dashboards with Interactivity, Sharing, Native Excel Exports, App Integration & more Get technology previously reserved for billion-dollar corporations, FREE http://pubads.g.doubleclick.net/gampad/clk?id=190641631&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
