• Methodological Guidelines
• Regular Papers vs. Technical Notes
• Multiple Submission Policy
• Formatting Instructions
• Where to Submit Manuscripts

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.

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.

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.

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.

1. Title page. Please list your name, affiliation, and complete address on the title page, providing a daytime telephone number, and an electronic mail address if available. Include a brief, one-paragraph abstract of 100-200 words and a list of six or fewer key words. Also include a shortened version of your title for use on page headings; identify this as the running head.

2. Text. Begin the text on a new page following the title page. Manuscripts should be printed on 8.5 x 11 inch paper (or A4 paper), single-sided and double-spaced, with pages numbered consecutively. Papers should be 8,000 to 12,000 words in length, with full-page figures counting for 400 words. Use footnotes sparingly, indicating them by consecutive numbers in the text. Include acknowledgements in a separate section at the end of the text.

3. References Authors should follow the APA Publication Manual 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. Here are some examples:

   Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986). SOAR: The anatomy of a general learning mechanism. Machine Learning, 1, 11-46.

   Quinlan, J. R. (1986). The effect of noise on concept learning. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 2). San Francisco, CA: Morgan Kaufmann.

   Schlimmer, J. C., & Fisher, D. H. (1986). A case study of incremental concept induction. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 496-501). San Francisco, CA: Morgan Kaufmann.

4. Figures and tables. Mention each figure and table in the text and number them consecutively using Arabic numerals. Embed them in the text if possible to ease the reviewing process. Figures should contain graphical material, whereas tables should contain tabular and typeset material (including pseudo-code for algorithms). Include a brief title above each table and a caption below each figure.

5. Spelling and terminology. Authors should employ technical terms with care, using existing terms when defined by earlier authors and carefully specifying the sense in which they intend ambiguous terms. Please keep abbreviations to a minimum. American spelling is preferred to British spelling.

6. Reprints and page charges. Authors of published papers will be provided with 50 reprints free of charge. No page charges will be levied.