Talk:Machine learning
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[edit] General discussion
I modified the strong claim that Machine Learning systems try to create programs without an engineer's intutition. When a machine learning task is specified, a human decides how the data are to be represented (e.g. which attributes will be used or how the data need to be preprocessed). This is the "observation language". The designer also decides the "hypothesis language", i.e. how the learned concept will be represented. Decision trees, neural nets, SVMs all have subtley different ways of describing the learned concept. The designer also decides on the kind of search that will be used, which biases the end result.
The way the page is written now, there is no distinguishing between machine learning and pattern recognition. machine learning is much more than simple classification. Robots that learn how to act in groups is machine learning but not pattern recognition. I am not an expert at ML, but am an expert in pattern recognition. So I hope that someone will edit this page and put in more information about machine learning that is not also pattern recognition.
- I don't agree with this: I believe that pattern recognition is generally restricted to classification, while this page explicitly says that ML covers classification, supervised learning (which includes regression), unsupervised learning (such as clustering), and reinforcement learning.
- Careful not to pigeonhole into the "unsupervised learning is clustering and vice versa". The data mining folks think this way and they're completely wrong, as my ML prof once said. User:65.50.71.194
- Notice that I said "such as clustering". The article does clearly state that unsupervised learning is modeling. -- hike395 16:02, 2 Mar 2005 (UTC)
- Careful not to pigeonhole into the "unsupervised learning is clustering and vice versa". The data mining folks think this way and they're completely wrong, as my ML prof once said. User:65.50.71.194
- Further, I don't think of pattern recognition as a specific method, but rather a collection of methods, generally described in the 1st edition of Duda and Hart. So, I deleted pattern recognition from "common methods". Also, a genetic algorithm is a generic optimization algorithm, not a machine learning algorithm. So, I removed it, too. -- hike395 01:13, 20 Dec 2004 (UTC)
- There are those who would disagree on the subject of Genetic Algorithms and their relation to ML. Machine learning takes it's basic principles from those found in naturally occurring systems, so do GA's. You could call evolution a kind of "intelligence", I suppose. Anyway the call's been made, but there should be some mention in the "related".
- I disagree with this statement --- machine learning has completely divorced itself from any natural "intelligent" system: it is a branch of statistics. I think you are thinking of the term "computational intelligence" (which is the new name for an IEEE society). I'm happy to have See also links to AI and CI. -- hike395 16:02, 2 Mar 2005 (UTC)
- There are those who would disagree on the subject of Genetic Algorithms and their relation to ML. Machine learning takes it's basic principles from those found in naturally occurring systems, so do GA's. You could call evolution a kind of "intelligence", I suppose. Anyway the call's been made, but there should be some mention in the "related".
>You could call evolution a kind of "intelligence"
No. Evolution is not goal-directed.
Blaise 17:32, 30 Apr 2005 (UTC)
Unlike many in the ML community, who want to find computationally lightweight algorithms that scale to very large data sets, many statisticians are currently interested in computationally intensive algorithms. (We're interested in getting models that are as faithful as possible to the situation, and we generally work with smaller data sets, so the scaling isn't such a big issue.) The point I'm making is that the statement that "ML is synonymous with computational statistics" is just plain wrong.
Blaise 17:29, 30 Apr 2005 (UTC)
- I had misgivings about that statement, too, so I just deleted it. Notice that I also deleted your edit that statistics deals with data uncertainty only, but ML deals with certain and uncertain data. I'd be willing to bet that you are a frequentist (right?). At the 50 kilometer level, frequentist statisticians deal with data uncertainty, but Bayesian statisticians deal with model uncertainty (keeping the observed data as an absolute, and integrating over different model parameters). I don't think you can make the distinction that statisticians are only frequentist (deal with data uncertainty), since Bayesian statisticians would violently disagree.
- Now, if you say that ML people care more about accurate predictions, while statisticians care more about accurate models, that may be true, although I don't believe you can make an absolute statement. --- hike395 23:02, 30 Apr 2005 (UTC)
[edit] Reinforcement Learning Placement
Shouldn't reinforcement learning be a subset of unsupervised learning?
- I don't think so. Reinforcement learning is not completely unsupervised: the algorithm has access to a supervision signal (the reward). It's just that it is difficult to determine which action(s) led to the reward, and there's an exploitation vs. exploration tradeoff. So, it isn't strictly supervised learning, either. It's somewhere in-between. -- hike395 July 1, 2005 07:08 (UTC)
[edit] Double
Perceptron its one of Artifical Neuronal Nets.
[edit] Radial basis function
Should this article link to the "radial basis function" article, instead of linking to the two articles "radial" and "basic function"?
[edit] Blogs
Some people, mainly researchers of this field (ML) are blogging about this subject. Some blogs are really interesting. Is there a space in an encyclopedia for links to those blogs ? I can see 3 pbs with this:
- advertising for people/blogs?
- how to select relevant blogs
- necessity to check if those blogs are enougth often updated.
What do you think of adding a blog links section ? Dangauthier 14:11, 13 March 2006 (UTC)
- Can be interesting, the question is of course which ones to include. I posted recently a list of machine learning blogs on my blog: http://www.inma.ucl.ac.be/~francois/blog/entries/entry_155.php Damienfrancois 09:09, 7 June 2006 (UTC)
I deleted the link to a supposed ML blog [1] which wasn't relevant, and was not in english.
- I oppose the inclusion of blogs. Most of the article right now consists of links. See WP:Linkspam --Adoniscik (talk) 21:01, 9 March 2008 (UTC)
[edit] Structured Data Mining is missing
The category Structured Data Mining is missing. See summarization Especially the sub-categories are also missing:
Two important books are:
- Kernel Methods in Computational Biology, Bernhard Scholkopf, Koji Tsuda, Jean-Philippe Vert
- Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology
JKW 11:50, 8 April 2006 (UTC)
[edit] deductive learning?
"At a general level, there are two types of learning: inductive, and deductive."
What's deductive learning? Isn't learning inductive? --Took 01:48, 10 April 2006 (UTC)
From a purely writing view, the rest of the paragraph (after the above quote) goes on to explain what inductive machine learning is, but deductive machine learning isn't covered at all. --Ferris37 03:49, 9 July 2006 (UTC)
[edit] Non-homogeneous reference format
It's a minor, but I see in this article the format of the references is inconsistent. Bishop is cited once as Christopher M. Bishop and another one as Bishop, C.M. Is there a standard format for wikipedia references? Jose
[edit] Help needed with "learn"
Hi,
In the following context
As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn"
,no definition of the last word in the sentense - "learn" - is given. However, it appears very essential, because it's central to this main definition.
A definition like "machine learning is an algorithm that allows machines to learn" sounds to me like a perfectly tautologous definition.
It's my understading that this article is about either computer science, or mathematics, or statistics, or some other "exact" discipline. All of these disciplines have quite exact definitions of everything, exept for those very few undefined terms that are declared upfront as axioms or undefined concepts. Examples: point, set, "Axiom of choice".
In this article, the purpose of Machine Learning and the tools it uses are clear to me as a reader. But the very method is obscure - what exactly it means for a machine to 'learn'. Would somebody please define "learn" in precise terms without resortiong to other obscure and not exactly defined in the technical world words like 'understand' or 'intelligence'?
There must exist a formal definition of 'learn', but if not, then, in my opinion, in order to avoid confusion, it should be clearly stated upfront that the very subject of machine learning is not clearly defined.
Compare this, for example, to how 'mathematics' is defined, or how the functions of ASIMO robot are clearly defined in Wikipedia.
Thanks in advance, Raokramer 13:28, 8 October 2007 (UTC)
[edit] Are there any learning algorithms that don't work by search?
Do all learning algorithms perform search? All rule/decision-tree algorithms certainly do search. Are there any exceptions?
- Naive bayes classifiers may be one exception as it is computed by a closed-form formula.
Are there any other exceptions? Pgr94 (talk) 12:31, 16 April 2008 (UTC)
