After reading your message, I agree, Andy, it's better to focus on the newer and more useful methods rather than adding those remnants from the past that no one really uses in practice anymore anyway. During the implementation of the VotingClassifier I kind of realized how much work is involved and thus it would be better to focus the energy and time on more important things. While it's probably not a bad idea to have the perceptron for educational purposes, also Bishop mentions the adaline only in a side note at the end of the perceptron article, thus I think no one would really miss this algorithm in scikit-learn ;)
Best, Sebastian > On Apr 5, 2015, at 11:00 AM, Andy <t3k...@gmail.com> wrote: > > Hi Sebastian. > First off, if this is a classification algorithm with sum of squared > errors, you can just do it using linear regression + OvRClassifier, right? > > In general, I (and I think most of the rest of the project) am weary > about adding something for "completeness". > Any algorithm we add creates a significant amount of maintenance burden. > See: > http://scikit-learn.org/dev/faq.html#can-i-add-this-classical-algorithm-from-the-80s > and > http://scikit-learn.org/dev/faq.html#why-are-you-so-selective-on-what-algorithms-you-include-in-scikit-learn > > Furthermore, I have not heard of this algorithm, and it is not mentioned > in any of the prominent textbooks (ESL, Bishop, Murphy). > So while it might be foundational, I don't think it is necessary for > "completeness". > > > Andy > > >> On 04/04/2015 12:16 AM, Sebastian Raschka wrote: >> Hi, >> e >> maybe I overlooked something, but I couldn't find the classic adaline >> (ADAptive LInear NEuron) in scikit-learn. It's probably not that useful >> (anymore) since we have logistic regression and support vector machines, but >> maybe it would not be a bad idea to add for the sake of completeness (and >> since scikit-learn also has a perceptron)? >> >> The implementation would be similar to logistic regression, but the cost >> function is the sum of the squared errors like in linear regression. It >> could be added to the SGDClassifier as loss='linear' or loss='adaline' plus >> a separate implementation using liblinear. >> >> The reference would be: >> B. Widrow et al. Adaptive ”Adaline” neuron using chemical ”memistors”. >> Number Technical Report 1553-2. Stanford Electron. Labs., Stanford, CA, >> October 1960 >> >> What do you think? >> >> Best, >> Sebastian >> ------------------------------------------------------------------------------ >> Dive into the World of Parallel Programming The Go Parallel Website, >> sponsored >> by Intel and developed in partnership with Slashdot Media, is your hub for >> all >> things parallel software development, from weekly thought leadership blogs to >> news, videos, case studies, tutorials and more. Take a look and join the >> conversation now. http://goparallel.sourceforge.net/ >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > ------------------------------------------------------------------------------ > Dive into the World of Parallel Programming The Go Parallel Website, sponsored > by Intel and developed in partnership with Slashdot Media, is your hub for all > things parallel software development, from weekly thought leadership blogs to > news, videos, case studies, tutorials and more. Take a look and join the > conversation now. http://goparallel.sourceforge.net/ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
