Machine Learning Journal: Information for Authors


Methodological Guidelines

Machine Learning publishes papers on a wide range of topics concerning computational approaches to learning, as indicated in the statement of Aims and Scope. Research on some of these topics--specifically, the development and experimental comparison of learning algorithms and the development and theoretical analysis of mathematical models of machine learning--has matured to the point that the Editorial Board has set forth the following methodological guidelines and recommendations for papers submitted to Machine Learning on these particular topics.

General Guidelines

  1. The main exposition of the paper should be aimed at readers who are generally familiar with machine learning concepts and ideas but not necessarily with any particular subarea of the field. In particular, the overall significance of the research results should be understandable to the general reader.

Guidelines for Experimental Papers

  1. Papers that introduce a new learning "setting" or type of application should justify the relevance and importance of this setting, for example, based on its utility in applications, its appropriateness as a model of human or animal learning, or its importance in addressing fundamental questions in machine learning.

  2. Papers describing a new algorithm should be clear, precise, and written in a way that allows the reader to compare the algorithm to other algorithms. For example, most learning algorithms can be viewed as optimizing (at least approximately) some measure of performance. A good way to describe a new algorithm is to make this performance measure explicit. Another useful way of describing an algorithm is to define the space of hypotheses that it searches when optimizing the performance measure.

  3. Papers introducing a new algorithm should conduct experiments comparing it to state-of-the-art algorithms for the same or similar problems. Where possible, performance should also be compared against an absolute standard of ideal performance. Performance should also be compared against a naive standard (e.g., random guessing, guessing the most common class, etc.) as well. Unusual performance criteria should be carefully defined and justified.

  4. All experiments must include measures of uncertainty of the conclusions. These typically take the form of confidence intervals, statistical tests, or estimates of standard error. Proper experimental methodology should be employed. For example, if "test sets" are used to measure generalization performance, no information from the test set should be available to the learning process.

  5. Descriptions of the software and data sufficient to replicate the experiments must be included in the paper. Once the paper has appeared in Machine Learning, authors are strongly urged to make the data used in experiments available to other scientists wishing to replicate the experiments. An excellent way to achieve this is to deposit the data sets at the Irvine Repository of Machine Learning Databases. Another good option is to add your data sets to the DELVE benchmark collection at the University of Toronto. For proprietary data sets, authors are encouraged to develop synthetic data sets having the same statistical properties. These synthetic data sets can then be made freely available.

  6. Conclusions drawn from a series of experimental runs should be clearly stated. Graphical display of experimental data can be very effective. Supporting tables of exact numerical results from experiments should be provided in an appendix.

  7. Limitations of the algorithm should be described in detail. Interesting cases where an algorithm fails are important in clarifying the range of applicability of an algorithm.

Guidelines for Theoretical Papers

  1. The "moral", or general meaning of technical theorems, should be explained and discussed. Comparisons with general methods in machine learning should be made.

  2. The overall consequences of the main theorems should balance the technical aspects of the paper. That is, a paper that has 30 pages of detailed mathematics had better have some deep consequences that are relevant to machine learning at large.

  3. The proof ideas, and the intuitions behind the proofs of theorems that are more than routine, should be explained.

Regular Papers versus Technical Notes

Most of the papers published in Machine Learning are regular papers that give in-depth treatment to a particular topic. However, Machine Learning also publishes Technical Notes. A technical note must be a self-contained, small contribution. Often it is a critique or response to something previously published in Machine Learning Other times it is a short note describing a modification or enhancement to an existing algorithm. Many technical notes could be published as conference papers instead, although even there they might not be accepted because their significance is often limited to a small audience interested in one particular algorithm.

On the other hand, many conference papers would not be appropriate technical notes, because their scope is broader and adequate (non-conference) treatment of the topic requires greater discussion of previous work, fuller description of experiments (so that they can be replicated), or complete proofs.

Multiple Submission Policy

Manuscripts submitted to Machine Learning must be unpublished original research. If related work has been previously published, the manuscript submitted to Machine Learning must involve significant revision or extension. Manuscripts submitted to Machine Learning must not be concurrently under review at any other journal. If the manuscript relies heavily on other unpublished manuscripts that are under review elsewhere, copies of these should be enclosed along with the manuscript so that reviewers can consult them.

Submission Instructions

It is helpful if manuscripts submitted to Machine Learning follow the same format as the final published versions. To assist in this authors may use the Kluwer LaTeX style files. Note that these are new style files (as of June, 2000) and in a new location. References should be in the APA reference format. for both the text and the reference list, with two exceptions: (a) do not cite the page numbers of any book, including chapters in edited volumes; (b) use the same format for unpublished references as for published ones. The Kluwer style files support the APA format, but authors may also choose to use the following Latex style files supporting the APA format:

Electronic submissions are encouraged - as of October 2001 these are to be emailed to Kluwer, not to the executive editor.

For complete instructions for submitting a manuscript - Click Here.

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