______________________________________________________________________
                      Paper Review Form
			      for
		       Machine Learning journal
	  Special Issue on Applications of Machine Learning
		 and the Knowledge Discovery Process


Title:							Code:
Author(s):						Date:


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APPROPRIATENESS:  Is this paper appropriate for this journal? 
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CONTRIBUTION: What contribution is made by this paper?  Assess the
              novelty of the contribution.  Assess the generality of
              the contribution.  How significant is the contribution
              from an applications perspective?  From a research
              perspective?  Will it stimulate future research?  Does
              it provide useful knowledge for future applications?

              Note that the contribution may be more general than the
              introduction of a new learning method.  Possible
              alternatives are: analyses of simplifying assumptions
              commonly made in machine learning literature that cannot
              be made in applications, comparisons of methods for
              addressing such simplifying assumptions, modifications
              of existing methods to address applications issues,
              methods for other aspects of the knowledge discovery
              process, analysis of why existing methods fail for a
              particular application, analysis of the overall process
              of applying machine learning methods, etc.
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DEFINITION & MOTIVATION:

Is the application task clearly defined? Is the need for machine
learning motivated well?  Are existing or alternative methods
discussed.  Are the important facets of the task identified in such a
way that the results will generalize to similar problems?  Is a class
of problems with similar characteristics defined?  Are the 
evaluation criteria well motivated from the perspective of the
application task (i.e., do they reflect the target task)?

Is the knowledge discovery process well defined?  Does the paper focus
on the entire knowledge discovery process, or a particular segment?

Are the methods clearly defined?  Is their use motivated well?  Is
their choice (over other methods) well justified?  Are the methods
specific to this problem, or will they generalize?  Are the
limitations on generalization enumerated?  Are the methods technically
sound?

Support:

How do the authors provide support for the paper's contribution (e.g.,
empirical evaluation, theoretical justification, demonstration, survey
of the literature, etc.).  Do they provide sufficient support for their
claims?  Is the argument technically & logically sound?

Discussion: 

Does the paper provide an adequate discussion of related work?  Does
it describe similarities, differences, and progress?  Does the paper
discuss the implications of its contribution? Does the paper argue for
a new research direction?  If so, does the paper provide an adequate
description of the current state of research in this direction? Does
the paper identify the limitations of its contribution and the
simplifying assumptions made?  Does the paper discuss lessons learned
in applying machine learning that are of general interest?

Does the paper discuss non-technical issues in applying machine
learning that may be generally useful?

General: 

Is the paper well-organized and well-written?  Does it use standard
terminology?  Has the author provided sufficient background?  Are an
appropriate number of informative figures included?  Are results
presented clearly?  Does the abstract adequately reflect the contents?


Does anything need to be added or deleted?

Recommendation:

	o accept
	o accept after minor revising
	o encourage resubmission to Machine Learning after
	  a major revision (not for special issue)
	o encourage submission to the journal: ______________
	o reject