Subject: CFP: Special Machine Learning issue on applications of ML
From: [email-masked] (Ronny Kohavi)
Date: 30 Oct 1996 23:52:38 -0800
Newsgroups: comp.ai

Call For Papers

                           Machine Learning 

                           Special Issue on
                   Applications of Machine Learning
                 and the Knowledge Discovery Process
				   
	    Guest editors: Ronny Kohavi and Foster Provost

   With the explosion in size of business and scientific databases
   (VLDBs), the opportunities and pressure to mine the data and make
   novel discoveries have increased dramatically.  For many problems,
   basic statistical summaries are not sufficient and there is a clear
   and recognized need for solutions involving a machine learning
   component. For example, modern businesses constantly seek to gain
   competitive advantage by tailoring actions to different customer
   segments and avoiding the trap of targeting the "average customer."

   This special issue of the journal Machine Learning will be dedicated
   to papers describing work in which machine learning technologies have
   been applied to solve significant real-world problems.  In particular,
   it will focus on the application of Machine Learning technology, the
   simplifying assumptions that *cannot* be made in a real-world
   application, and the processes that are involved in going from the raw
   data to the final knowledge that decision makers seek.

                  --------------- Scope ---------------

   High quality, original papers that address applications of machine
   learning technologies to significant, real-world problems are
   solicited.  Authors are required to describe the papers' scientific
   contributions clearly.  We actively solicit papers that address
   contributions not normally published in the machine learning
   literature, including (but not limited to):

   - - Applications of machine learning to real-world significant problems:
     successes, failures, limitations, and lessons learned.

   - - The knowledge discovery process: it is estimated that only
     20% of the overall process is spent on running machine
     learning algorithms.  What other things were done?  What
     lessons were learned? Which parts of the overall process could be
     improved and perhaps automated?

   - - A novel solution to a non-trivial _class_ of applications.  Such a 
     paper would include: a description of the class of applications,
     detailing why they are different from previously solved
     applications; an "existence proof" on one member of the class;
     and a solid argument as to the generality of the solution for
     the class of applications.

   - - The identification of a number of assumptions normally made within
     machine learning research that cannot be made for this application,
     and a thorough description of how they were addressed (simultaneously)
     to engineer a working solution.

   - - A principled study of the relaxation of important assumptions
     necessary for the solution to the problem.

   - - Novel uses of existing algorithms and modifications
     to broaden their scope and overcome limitations and assumptions
     that hinder their use in real applications.

   Authors should address the following issues, when relevant:

   - - The real-world problem and its formulation as a machine learning
     task.  How significant was the problem?
   - - Who are the users of the learned knowledge?  Who "paid" for the work? 
     How did their requirements constrain the knowledge discovery effort? 
     How was success to be determined and was the expected
     payoff estimated in advance?  
   - - The data selection process.  Was a sampling process used or were all 
     the data used?  What were reasons for limiting the amount of data 
     (resource constraints, simplicity of result, learning curve flattened 
     early)?
   - - Data transformations.  How were the raw data transformed into
     formats suitable for existing algorithms?  What processes were required,
     why were certain transformations done, and were others tried?
     Examples: data cleaning, missing values, data transformations,
     aggregrations, group signatures/profiles,  denormalization,
     and feature construction.
   - - Why were specific algorithms chosen, and which others were tried?
   - - What role did background knowledge play and how has it affected
     the process?
   - - What were the post-processing operations?  How were the results
     explained to users (visualizations, dimensionality reduction,
     explanations, what-if scenarios, sensitivity analyses)?
   - - How did the results affect the target users?  Was the mining done
     repeatedly, or was this a one-shot task?  Was the learned knowledge
     or the learning system fielded and integrated in other processes?

   The contribution of each paper should be sufficient to lay the
   groundwork for future work on the topic.  Future applied work can
   build on the initial solutions described; future academic work can
   explore the exposed assumptions in depth and provide more principled
   solutions.  We encourage authors to suggest "challege problems" for
   future academic work.  For example, can a useful "transformation
   language" be developed in a manner similar to the role relational
   calculus has served for relational databases?

   We hope that by providing the machine learning community with such a
   forum, we can stimulate the applied/academic cycle that is important
   for the healthy growth of a scientific field.

           --------------- Submission Requirements ---------------

   The Machine Learning journal is published by Kluwer Academic
   Publishers.  Electronic submissions are ENCOURAGED; postscript copies
   may be e-mailed to [email-masked].  Latex style file and
   related files available via anonymous ftp from ftp.std.com in
   Kluwer/styles/journals.

   For non-electronic submissions, send six (6) copies of the papers
   as indicated:

         Five (5) copies to:                      One (1) copy to:

         Mrs. Karen Cullen                         Ronny Kohavi
          MACHINE LEARNING		      Silicon Graphics, Inc, M/S 8U-876
     Kluwer Academic Publishers	        2011 N. Shoreline Boulevard
         101 Philip Drive			     Mountain View, CA 94043
          Assinippi Park
         Norwell, MA 02061			    E-mail: [email-masked]
                                                 Tel: 415-933-3126

   Manuscripts should be no longer than 8000-12000 words with full-page
   figures counting for 400 words.  Shorter submissions, including
   technical notes are also solicited. The title page should include names,
   affiliations, and complete address including daytime telephone number
   and an electronic e-mail address of the contact author.  Include a
   brief, one-paragraph abstract of 100-200 words and a list of keywords.

   Submissions must not have appeared in, nor be under consideration by,
   other journals.

           --------------- Important Dates --------------------

           Submission deadline:		4 Mar  1997
           Acceptance notification:	3 June 1997

   ** Review criteria and further details will be sent in the future.

   Specific questions and clarifications should be sent to
                    [email-masked]