%%% -*-BibTeX-*-
%%% ====================================================================
%%% BibTeX-file{
%%% author = "Christopher Hugh Bryant",
%%% version = "1.05",
%%% date = "25 November 2011",
%%% time = "15:50:10 MDT",
%%% filename = "bryant-chris.bib",
%%% address = "The Robert Gordon University
%%% School of Computing
%%% St Andrew St, Aberdeen
%%% AB25 1HG
%%% Scotland, UK",
%%% telephone = "+441224 262737",
%%% FAX = "+441224 262727",
%%% URL = "http://www.scms.rgu.ac.uk/staff/chb",
%%% checksum = "64798 681 3744 35436",
%%% email = "chb at scms.rgu.ac.uk (Internet)",
%%% codetable = "ISO/ASCII",
%%% keywords = "bibliography, BibTeX",
%%% license = "public domain",
%%% supported = "yes",
%%% docstring = "This is a bibliography of publications of
%%% Christopher Hugh Bryant. The companion LaTeX file
%%% bryant-christopher-h.ltx can be used to typeset
%%% this bibliography.
%%%
%%% At version 1.05, the year coverage looked
%%% like this:
%%%
%%% 1994 ( 1) 1997 ( 5) 2000 ( 6)
%%% 1995 ( 2) 1998 ( 1) 2001 ( 3)
%%% 1996 ( 2) 1999 ( 1)
%%%
%%% Article: 7
%%% Booklet: 1
%%% InProceedings: 9
%%% Proceedings: 4
%%%
%%% Total entries: 21
%%%
%%% This file is available as part of the BibNet
%%% Project. The master copy is available for
%%% public access on ftp.math.utah.edu in the
%%% directory tree /pub/bibnet/authors. It is
%%% mirrored to netlib.bell-labs.com in the directory
%%% tree /netlib/bibnet/authors, from which it is
%%% available via anonymous ftp and the Netlib
%%% service.
%%%
%%% The checksum field above contains a CRC-16
%%% checksum as the first value, followed by the
%%% equivalent of the standard UNIX wc (word
%%% count) utility output of lines, words, and
%%% characters. This is produced by Robert
%%% Solovay's checksum utility.",
%%% }
%%% ====================================================================
%%% ====================================================================
%%% Publisher abbreviations:
@String{pub-MORGAN-KAUFMANN = "Morgan Kaufmann Publishers"}
@String{pub-MORGAN-KAUFMANN:adr = "San Francisco, CA, USA"}
@String{pub-SV = "Springer-Verlag"}
@String{pub-SV:adr = "Berlin, Germany~/ Heidelberg, Germany~/
London, UK~/ etc."}
%%% ====================================================================
%%% Series abbreviations:
@String{ser-LNAI = "Lecture Notes in Artificial Intelligence"}
@String{ser-LNCS = "Lecture Notes in Computer Science"}
%%% ====================================================================
%%% Bibliography entries:
@Article{Bryant:1994:RES,
author = "C. H. Bryant and A. E. Adam and D. R. Taylor and R. C.
Rowe",
title = "Review of Expert Systems for Chromatography",
journal = "Analytica Chimica Acta",
volume = "297",
number = "3",
pages = "317--347",
year = "1994",
CODEN = "ACACAM",
ISSN = "0003-2670",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_aca_review.ps.gz",
abstract = "Expert systems for chromatography are reviewed. A
taxonomy is proposed that allows present (and future)
expert systems in this area to be classified and
facilitates an understanding of their
inter-relationship. All the systems are described
focusing on the reasons for their development, what
their purpose was and how they were to be used. The
engineering methods, knowledge representations, tools
and architectures used for the systems are compared and
contrasted in a discussion covering all the stages of
the development life cycle of expert systems. The
review reveals that too often developers of expert
systems for chromatography do not justify their
decisions on engineering matters and that the
literature suggests that many ideas advocated by
knowledge engineers are not being used.",
}
@InProceedings{Bryant:1995:DCA,
author = "C. H. Bryant and A. E. Adam and D. R. Taylor and G. V.
Conroy and R. C. Rowe",
booktitle = "Data Mining",
title = "{DataMariner}, a Commercially Available Data Mining
Package, and its Application to a Chemistry Domain",
publisher = "UNICOM",
address = "London, UK",
year = "1995",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
}
@InProceedings{Bryant:1995:DKH,
author = "C. H. Bryant and A. E. Adam and D. R. Taylor and G. V.
Conroy and R. C. Rowe",
booktitle = "Knowledge Discovery in Databases",
title = "Discovering Knowledge Hidden in a Chemical Database
Using a Commercially Available {Data Mining} Tool",
number = "Digest 1995/021(B)",
publisher = "????",
address = "Savoy Place, London, WC2R OBL, UK",
year = "1995",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
series = "IEE Computing and Control Division",
}
@Article{Bryant:1996:TES,
author = "C. H. Bryant and A. E. Adam and D. R. Taylor and R. C.
Rowe",
title = "Towards an Expert System for Enantioseparations:
Induction of Rules Using Machine Learning",
journal = "Chemometrics and Intelligent Laboratory Systems",
volume = "34",
number = "1",
pages = "21--40",
year = "1996",
CODEN = "CILSEN",
ISSN = "0169-7439",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_DataMariner.ps.gz",
abstract = "A commercially available machine induction tool was
used in an attempt to automate the acquisition of the
knowledge needed for an expert system for
enantioseparations by High Performance Liquid
Chromatography using Pirkle-type chiral stationary
phases (CSPs). Various rule-sets were induced that
recommended particular CSP chiral selectors based on
the structural features of an enantiomer pair. The
results suggest that the accuracy of the optimal
rule-set is 63\% + or - 3\% which is more than ten
times greater than the accuracy that would have
resulted from a random choice.",
}
@InProceedings{McCluskey:1996:VFS,
author = "T. L. McCluskey and J. M. Porteous and M. M. West and
C. H. Bryant",
booktitle = "Proceedings of the BCS-FACS Northern Formal Methods
Workshop, Ilkley, UK",
title = "The Validation of Formal Specifications of
Requirements",
publisher = pub-SV,
address = pub-SV:adr,
month = sep,
year = "1996",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
series = "Electronic Workshops in Computing Series",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_north_fm_ws.ps.gz",
}
@Booklet{Bryant:1997:CGR,
author = "C. H. Bryant",
title = "Computer Generation of Rules for an Expert System for
Enantioseparations",
howpublished = "Invited presentation given at Chrial Technology and
Enantioseparations '97",
address = "Cambridge, UK",
month = apr,
year = "1997",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
}
@InProceedings{Bryant:1997:DMI,
author = "C. H. Bryant",
title = "{Data Mining} via {ILP}: The Application of {Progol}
to a Database of Enantioseparations",
crossref = "Lavrac:1997:ILP",
pages = "85--92",
year = "1997",
bibdate = "Thu Apr 4 13:44:03 MST 2002",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
series = "Lecture Notes in Artificial Intelligence",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_ilp97.ps.gz",
abstract = "As far as this author is aware, this is the first
paper to describe the application of Progol to
enantioseparations. A scheme is proposed for data
mining a relational database of published
enantioseparations using Progol. The application of the
scheme is described and a preliminary assessment of the
usefulness of the resulting generalisations is made
using their accuracy, size, ease of interpretation and
chemical justification.",
}
@Article{Bryant:1997:UIL,
author = "C. H. Bryant and A. E. Adam and D. R. Taylor and R. C.
Rowe",
title = "Using {Inductive Logic Programming} to Discover
Knowledge Hidden in Chemical Data",
journal = "Chemometrics and Intelligent Laboratory Systems",
volume = "36",
number = "2",
pages = "111--123",
year = "1997",
CODEN = "CILSEN",
ISSN = "0169-7439",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_golem.ps.gz",
abstract = "This paper demonstrates how general purpose tools from
the field of Inductive Logic Programming (ILP) can be
applied to analytical chemistry. As far as these
authors are aware, this is the first published work to
describe the application of the ILP tool Golem to
separation science. An outline of the theory of ILP is
given, together with a description of Golem and
previous applications of ILP. The advantages of ILP
over classical machine induction techniques, such as
the Top-Down-Induction-of-Decision-Tree family, are
explained. A case-study is then presented in which
Golem is used to induce rules which predict, with a
high accuracy (82\%), whether each of a series of
attempted separations succeed or fail. The separation
data was obtained from published work on the attempted
separation of a series of 3-substituted phthalide
enantiomer pairs on
(R)-N-(3,5-dinitrobenzoyl)-phenylglycine.",
}
@InProceedings{West:1997:TGP,
author = "M. M. West and C. H. Bryant and T. L. McCluskey",
booktitle = "The preliminary Proceedings of the Seventh
International Workshop on Logic Program Synthesis and
Transformation",
title = "Transforming General Program Proofs: {A} Meta
Interpreter which Expands Negative Literals",
publisher = "????",
address = "Leuven, Belgium",
year = "1997",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_lopstr97.ps.gz",
}
@Article{Bryant:1998:KDD,
author = "C. H. Bryant and R. C. Rowe",
title = "{Knowledge Discovery} in {Databases}: Application to
Chromatography",
journal = "Trends in Analytical Chemistry",
volume = "17",
pages = "18--24",
month = "1",
year = "1998",
CODEN = "TTAEDJ",
ISSN = "0165-9936",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_TRAC.ps.gz",
abstract = "This paper reviews emerging computer techniques for
discovering knowledge from databases and their
application to various sets of separation data. The
data-sets include the separation of a diverse range of
analytes using either liquid, gas or ion
chromatography. The main conclusion is that the new
techniques should help to close the gap between the
rate at which chromatographic data is gathered and
stored electronically and the rate at which it can be
analysed and understood.",
}
@InProceedings{Bryant:1999:CAL,
author = "C. H. Bryant and S. H. Muggleton and C. D. Page and M.
J. E. Sternberg",
editor = "S. Colton",
booktitle = "Proceedings of AISB'99 Symposium on AI and Scientific
Creativity",
title = "Combining {Active Learning} with {Inductive Logic
Programming} to close the loop in {Machine Learning}",
publisher = "The Society for the Study of Artificial Intelligence
and Simulation of Behaviour (AISB)",
address = "",
pages = "59--64",
year = "1999",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_aisb99.ps.gz;
http://www.cogs.susx.ac.uk/aisb/",
abstract = "Machine Learning (ML) systems that produce
human-comprehensible hypotheses from data are typically
open loop, with no direct link between the ML system
and the collection of data. This paper describes the
alternative, {\it Closed Loop Machine Learning}. This
is related to the area of Active Learning in which the
ML system actively selects experiments to discriminate
between contending hypotheses. In Closed Loop Machine
Learning the system not only selects but also carries
out the experiments in the learning domain. ASE-Progol,
a Closed Loop Machine Learning system, is proposed.
ASE-Progol will use the ILP system Progol to form the
initial hypothesis set. It will then devise experiments
to select between competing hypotheses, direct a robot
to perform the experiments, and finally analyse the
experimental results. ASE-Progol will then revise its
hypotheses and repeat the cycle until a unique
hypothesis remains. This will be, to our knowledge, the
first attempt to use a robot to carry out experiments
selected by Active Learning within a real world
application.",
}
@InProceedings{Muggleton:2000:LCL,
author = "S. H. Muggleton and C. H. Bryant and A. Srinivasan",
title = "Learning {Chomsky}-like Grammars for Biological
Sequence Families",
crossref = "Langley:2000:PSI",
pages = "631--638",
year = "2000",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_icml2k.ps.gz",
abstract = "This paper presents a new method of measuring
performance when positives are rare and investigates
whether Chomsky-like grammar representations are useful
for learning accurate comprehensible predictors of
members of biological sequence families. The
positive-only learning framework of the Inductive Logic
Programming (ILP) system CProgol is used to generate a
grammar for recognising a class of proteins known as
human neuropeptide precursors (NPPs). As far as these
authors are aware, this is both the first biological
grammar learnt using ILP and the first real-world
scientific application of the positive-only learning
framework of CProgol. Performance is measured using
both predictive accuracy and a new cost function, {\em
Relative Advantage\/} ($RA$). The $RA$ results show
that searching for NPPs by using our best NPP predictor
as a filter is more than 100 times more efficient than
randomly selecting proteins for synthesis and testing
them for biological activity. The highest $RA$ was
achieved by a model which includes grammar-derived
features. This $RA$ is significantly higher than the
best $RA$ achieved without the use of the
grammar-derived features.",
}
@InProceedings{Muggleton:2000:MPW,
author = "S. H. Muggleton and C. H. Bryant and A. Srinivasan",
title = "Measuring Performance when Positives are Rare:
Relative Advantage versus Predictive Accuracy --- a
Biological Case-study",
crossref = "LopezdeMantaras:2000:MLE",
pages = "300--312",
year = "2000",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_ecml2k.ps.gz;
http://www.springer.de/comp/lncs/index.html",
abstract = "This paper presents a new method of measuring
performance when positives are rare and investigates
whether Chomsky-like grammar representations are useful
for learning accurate comprehensible predictors of
members of biological sequence families. The
positive-only learning framework of the Inductive Logic
Programming (ILP) system CProgol is used to generate a
grammar for recognising a class of proteins known as
human neuropeptide precursors (NPPs). Performance is
measured using both predictive accuracy and a new cost
function, {\em Relative Advantage\/} ($RA$). The $RA$
results show that searching for NPPs by using our best
NPP predictor as a filter is more than 100 times more
efficient than randomly selecting proteins for
synthesis and testing them for biological activity.
Predictive accuracy is not a good measure of
performance for this domain because it does not
discriminate well between NPP recognition models:
despite covering varying numbers of (the rare)
positives, all the models are awarded a similar (high)
score by predictive accuracy because they all exclude
most of the abundant negatives.",
}
@InProceedings{Muggleton:2000:TCU,
author = "S. H. Muggleton and C. H. Bryant",
title = "Theory Completion using Inverse Entailment",
crossref = "Cussens:2000:ILP",
pages = "130--146",
year = "2000",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_ilp2k.ps.gz;
http://www.springer.de/comp/lncs/index.html",
abstract = "The main real-world applications of Inductive Logic
Programming (ILP) to date involve the ``Observation
Predicate Learning'' (OPL) assumption, in which both
the examples and hypotheses define the same predicate.
However, in both scientific discovery and language
learning potential applications exist in which OPL does
not hold. OPL is ingrained within the theory and
performance testing of Machine Learning. A general ILP
technique called ``Theory Completion using Inverse
Entailment'' (TCIE) is introduced which is applicable
to non-OPL applications. TCIE is based on inverse
entailment and is closely allied to abductive
inference. The implementation of TCIE within Progol5.0
is described. The implementation uses contra-positives
in a similar way to Stickel's Prolog Technology Theorem
Prover. Progol5.0 is tested on two different data-sets.
The first dataset involves a grammar which translates
numbers to their representation in English. The second
dataset involves hypothesising the function of unknown
genes within a network of metabolic pathways. On both
datasets near complete recovery of performance is
achieved after relearning when randomly chosen portions
of background knowledge are removed. Progol5.0's
running times for experiments in this paper were
typically under 6 seconds on a standard laptop PC.",
}
@Article{Bryant:2001:CIL,
author = "C. H. Bryant and S. H. Muggleton and S. G. Oliver and
D. B. Kell and P. Reiser and R. D. King",
title = "{Combining Inductive Logic} Programming, {Active
Learning} and Robotics to Discover the Function of
Genes",
journal = "Electronic Transactions on Artificial Intelligence",
volume = "5",
number = "B",
pages = "1--36",
year = "2001",
CODEN = "????",
ISSN = "????",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "http://www.ep.liu.se/ej/etai/2001/001/",
abstract = "The paper is addressed to AI workers with an interest
in biomolecular genetics and also to biomolecular
geneticists interested in what AI tools may do for
them. The authors are engaged in a collaborative
enterprise aimed at partially automating some aspects
of scientific work. These aspects include the processes
of forming hypotheses, devising trials to discriminate
between these competing hypotheses, physically
performing these trials and then using the results of
these trials to converge upon an accurate hypothesis.
As a potential component of the reasoning carried out
by an ``artificial scientist'' this paper describes
ASE-Progol, an Active Learning system which uses
Inductive Logic Programming to construct hypothesised
first-order theories and uses a CART-like algorithm to
select trials for eliminating ILP derived hypotheses.
In simulated yeast growth tests ASE-Progol was used to
rediscover how genes participate in the aromatic amino
acid pathway of {\em Saccharomyces cerevisiae}. The
cost of the chemicals consumed in converging upon a
hypothesis with an accuracy of around $88\%$ was
reduced by five orders of magnitude when trials were
selected by ASE-Progol rather than being sampled at
random. While the naive strategy of always choosing the
cheapest trial from the set of candidate trials led to
lower cumulative costs than ASE-Progol, both the naive
strategy and the random strategy took significantly
longer to converge upon a final hypothesis than
ASE-Progol. For example to reach an accuracy of $80\%$,
ASE-Progol required 4 days while random sampling
required 6 days and the naive strategy required 10
days.",
}
@Article{Muggleton:2001:GRU,
author = "S. H. Muggleton and C. H. Bryant and A. Srinivasan and
A. Whittaker and S. Topp and C. Rawlings",
title = "Are grammatical representations useful for learning
from biological sequence data? --- a case study",
journal = "Journal of Computational Biology",
volume = "8",
number = "5",
pages = "493--522",
month = oct,
year = "2001",
CODEN = "JCOBEM",
ISSN = "1066-5277",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
note = "\copyright Mary Ann Liebert.",
URL = "http://www.liebertpub.com/",
abstract = "This paper investigates whether Chomsky-like grammar
representations are useful for learning cost-effective,
comprehensible predictors of members of biological
sequence families. The Inductive Logic Programming
(ILP) Bayesian approach to learning from positive
examples is used to generate a grammar for recognising
a class of proteins known as human neuropeptide
precursors (NPPs). Collectively, five of the co-authors
of this paper, have extensive expertise on NPPs and
general bioinformatics methods. Their motivation for
generating a NPP grammar was that none of the existing
bioinformatics methods could provide sufficient
cost-savings during the search for new NPPs. Prior to
this project experienced specialists at SmithKline
Beecham had tried for many months to hand-code such a
grammar but without success. Our best predictor makes
the search for novel NPPs {\bf more than 100 times more
efficient} than randomly selecting proteins for
synthesis and testing them for biological activity. As
far as these authors are aware, this is both the first
biological grammar learnt using ILP and the first
real-world scientific application of the ILP Bayesian
approach to learning from positive examples. A group of
features is derived from this grammar. Other groups of
features of NPPs are derived using other learning
strategies. Amalgams of these groups are formed. A
recognition model is generated for each amalgam using
C4.5 and C4.5rules and its performance is measured
using both predictive accuracy and a new cost function,
{\em Relative Advantage\/} ($RA$). The highest $RA$ was
achieved by a model which includes grammar-derived
features. This $RA$ is significantly higher than the
best $RA$ achieved without the use of the
grammar-derived features. Predictive accuracy is not a
good measure of performance for this domain because it
does not discriminate well between NPP recognition
models: despite covering varying numbers of (the rare)
positives, all the models are awarded a similar (high)
score by predictive accuracy because they all exclude
most of the abundant negatives.",
finaldraft = "ftp://www.comp.rgu.ac.uk/pub/staff/chb/bryant_jcb.ps.gz",
}
@Article{Reiser:2001:DLM,
author = "P. Reiser and R. D. King and D. B. Kell and S. H.
Muggleton and C. H. Bryant and S. G. Oliver",
title = "Developing a Logical Model of Yeast Metabolism",
journal = "Electronic Transactions on Artificial Intelligence",
volume = "5",
number = "B",
pages = "223--244",
year = "2001",
CODEN = "????",
ISSN = "????",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
URL = "http://www.ep.liu.se/ej/etai/2001/013/",
abstract = "With the completion of the sequencing of genomes of
increasing numbers of organisms, the focus of biology
is moving to determining the role of these genes
(functional genomics). To this end it is useful to view
the cell as a biochemical machine: it consumes simple
molecules to manufacture more complex ones by chaining
together biochemical reactions into long sequences
referred to as {\em metabolic pathways}. Such metabolic
pathways are not linear but often intersect to form
complex networks. Genes play a fundamental role in
these networks by providing the information to
synthesise the enzymes that catalyse biochemical
reactions. Although developing a complete model of
metabolism is of fundamental importance to biology and
medicine, the size and complexity of the network has
proven beyond the capacity of human reasoning. This
paper presents the first results of the Robot Scientist
research programme that aims to automatically discover
the function of genes in the metabolism of the yeast
{\em Saccharomyces cerevisiae}. Results include: (1)
the first logical model of metabolism; (2) a method to
predict phenotype by deductive inference; and (3) a
method to infer reactions and gene function by
abductive inference. We describe the {\em in vivo\/}
experimental set-up which will allow these {\em in
silico\/} predictions to be automatically tested by a
laboratory robot.",
}
%%% ====================================================================
%%% Cross-referenced entries must come last:
@Proceedings{Lavrac:1997:ILP,
editor = "Nada Lavrac and Saso Dzeroski",
booktitle = "Inductive logic programming: 7th international
workshop, {ILP}-97, Prague, Czech Republic, September
17--20, 1997: proceedings",
title = "Inductive logic programming: 7th international
workshop, {ILP}-97, Prague, Czech Republic, September
17--20, 1997: proceedings",
volume = "1297",
publisher = pub-SV,
address = pub-SV:adr,
pages = "viii + 308",
year = "1997",
CODEN = "LNCSD9",
ISBN = "3-540-63514-9 (softcover)",
ISBN-13 = "978-3-540-63514-7 (softcover)",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA76.63.I52 1997",
bibdate = "Mon Nov 24 11:33:24 1997",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
series = ser-LNAI # " and " # ser-LNCS,
acknowledgement = ack-nhfb,
annote = "Revised versions of papers presented at the
workshop.",
keywords = "Logic programming --- Congresses.",
}
@Proceedings{Cussens:2000:ILP,
editor = "James Cussens and Alan Frisch",
booktitle = "Inductive logic programming: 10th International
Conference, {ILP} 2000, London, {UK}, July 2000:
proceedings",
title = "Inductive logic programming: 10th International
Conference, {ILP} 2000, London, {UK}, July 2000:
proceedings",
volume = "1866",
publisher = pub-SV,
address = pub-SV:adr,
pages = "x + 264",
year = "2000",
ISBN = "3-540-67795-X (softcover)",
ISBN-13 = "978-3-540-67795-6 (softcover)",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA267.A1 L43 no.1866",
bibdate = "Mon Oct 16 18:31:56 MDT 2000",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
series = ser-LNCS # " and " # ser-LNAI,
acknowledgement = ack-nhfb,
keywords = "logic programming -- congresses",
}
@Proceedings{Langley:2000:PSI,
editor = "Pat Langley",
booktitle = "Proceedings of the Seventeenth International
Conference on Machine Learning (ICML-2000), June
29--July 2, 2000, Stanford University",
title = "Proceedings of the Seventeenth International
Conference on Machine Learning ({ICML}-2000), June
29--July 2, 2000, Stanford University",
publisher = pub-MORGAN-KAUFMANN,
address = pub-MORGAN-KAUFMANN:adr,
pages = "xiv + 1219",
year = "2000",
ISBN = "1-55860-707-2",
ISBN-13 = "978-1-55860-707-1",
LCCN = "Q325.5 .I57 2000",
bibdate = "Thu Apr 04 13:57:19 2002",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
acknowledgement = ack-nhfb,
}
@Proceedings{LopezdeMantaras:2000:MLE,
editor = "Ramon {Lopez de Mantaras} and Enric Plaza",
booktitle = "Machine learning: {ECML} 2000: 11th European
Conference on Machine Learning, Barcelona, Catalonia,
Spain, May 31--June 2, 2000",
title = "Machine learning: {ECML} 2000: 11th European
Conference on Machine Learning, Barcelona, Catalonia,
Spain, May 31--June 2, 2000",
volume = "1810",
publisher = pub-SV,
address = pub-SV:adr,
pages = "xii + 460",
year = "2000",
ISBN = "3-540-67602-3",
ISBN-13 = "978-3-540-67602-7",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
LCCN = "QA267.A1 L43 no.1810",
bibdate = "Thu Apr 04 14:00:45 2002",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/b/bryant-chris.bib",
series = ser-LNCS # " and " # ser-LNAI,
acknowledgement = ack-nhfb,
keywords = "machine learning -- congresses; machine learning --
industrial applications -- congresses",
}