machine learning for science: state of the art and...

DNA to be sequences into distinct pieces,parcel out the detailed work of sequencing,and then reassemble these independent ef-forts at the end. It is not quite so simple in theworld of genome semantics.

Despite the differences between genome se-quencing and genetic network discovery, thereare clear parallels that are illustrated in Table 1.In genome sequencing, a physical map is usefulto provide scaffolding for assembling the fin-ished sequence. In the case of a genetic regula-

tory network, a graphical model can play thesame role. A graphical model can represent ahigh-level view of interconnectivity and helpisolate modules that can be studied indepen-dently. Like contigs in a genomic sequencingproject, low-level functional models can ex-plore the detailed behavior of a module of genesin a manner that is consistent with the higherlevel graphical model of the system. With stan-dardized nomenclature and compatible model-ing techniques, independent functional modelscan be assembled into a complete model of thecell under study.

To enable this process, there will need tobe standardized forms for model representa-tion. At present, there are many differentmodeling technologies in use, and althoughmodels can be easily placed into a database,they are not useful out of the context of theirspecific modeling package. The need for astandardized way of communicating compu-tational descriptions of biological systems ex-tends to the literature. Entire conferenceshave been established to explore ways ofmining the biology literature to extract se-

mantic information in computational form.Going forward, as a community we need

to come to consensus on how to representwhat we know about biology in computa-tional form as well as in words. The key topostgenomic biology will be the computa-tional assembly of our collective knowl-edge into a cohesive picture of cellular andorganism function. With such a comprehen-sive model, we will be able to explore newtypes of conservation between organismsand make great strides toward new thera-peutics that function on well-characterizedpathways.

References1. S. K. Kim et al., Science 293, 2087 (2001).2. A. Hartemink et al., paper presented at the Pacific

Symposium on Biocomputing 2000, Oahu, Hawaii, 4to 9 January 2000.

3. D. Pe’er et al., paper presented at the 9th Conferenceon Intelligent Systems in Molecular Biology (ISMB),Copenhagen, Denmark, 21 to 25 July 2001.

4. H. McAdams, A. Arkin, Proc. Natl. Acad. Sci. U.S.A.94, 814 ( 1997 ).

5. A. J. Hartemink, thesis, Massachusetts Institute ofTechnology, Cambridge (2001).

V I E W P O I N T

Machine Learning for Science: State of theArt and Future Prospects

Eric Mjolsness* and Dennis DeCoste

Recent advances in machine learning methods, along with successfulapplications across a wide variety of fields such as planetary science andbioinformatics, promise powerful new tools for practicing scientists. Thisviewpoint highlights some useful characteristics of modern machine learn-ing methods and their relevance to scientific applications. We concludewith some speculations on near-term progress and promising directions.

Machine learning (ML) (1) is the study ofcomputer algorithms capable of learning to im-prove their performance of a task on the basis oftheir own previous experience. The field isclosely related to pattern recognition and statis-tical inference. As an engineering field, ML hasbecome steadily more mathematical and moresuccessful in applications over the past 20years. Learning approaches such as data clus-tering, neural network classifiers, and nonlinearregression have found surprisingly wide appli-cation in the practice of engineering, business,and science. A generalized version of the stan-dard Hidden Markov Models of ML practicehave been used for ab initio prediction of genestructures in genomic DNA (2). The predictions

correlate surprisingly well with subsequentgene expression analysis (3). Postgenomic bi-ology prominently features large-scale gene ex-pression data analyzed by clustering methods(4), a standard topic in unsupervised learning.Many other examples can be given of learningand pattern recognition applications in science.Where will this trend lead? We believe it willlead to appropriate, partial automation of everyelement of scientific method, from hypothesisgeneration to model construction to decisiveexperimentation. Thus, ML has the potential toamplify every aspect of a working scientist’sprogress to understanding. It will also, for betteror worse, endow intelligent computer systemswith some of the general analytic power ofscientific thinking.

Machine Learning at Every Stage ofthe Scientific ProcessEach scientific field has its own version of thescientific process. But the cycle of observing,

creating hypotheses, testing by decisive exper-iment or observation, and iteratively buildingup comprehensive testable models or theories isshared across disciplines. For each stage of thisabstracted scientific process, there are relevantdevelopments in ML, statistical inference, andpattern recognition that will lead to semiauto-matic support tools of unknown but potentiallybroad applicability.

Increasingly, the early elements of scientificmethod—observation and hypothesis genera-tion—face high data volumes, high data acqui-sition rates, or requirements for objective anal-ysis that cannot be handled by human percep-tion alone. This has been the situation in exper-imental particle physics for decades. Thereautomatic pattern recognition for significantevents is well developed, including Houghtransforms, which are foundational in patternrecognition. A recent example is event analysisfor Cherenkov detectors (8) used in neutrinooscillation experiments. Microscope imagery incell biology, pathology, petrology, and otherfields has led to image-processing specialties.So has remote sensing from Earth-observingsatellites, such as the newly operational Terraspacecraft with its ASTER (a multispectralthermal radiometer), MISR (multiangle imag-ing spectral radiometer), MODIS (imaging

Machine Learning Systems Group, Jet Propulsion Lab-oratory/California Institute of Technology, Pasadena,CA, 91109, USA.

*To whom correspondence should be addressed. E-mail: [email protected]

Table 1. Parallels between genome sequencingand genetic network discovery.

Genomesequencing

Genome semantics

Physical maps Graphical modelContigs Low-level functional

modelsContig

reassemblyModule assembly

Finished genomesequence

Comprehensive model

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C O M P U T E R S A N D S C I E N C E

spectrometer), and other high–data-rate instru-ments that will require pattern recognition forsignal extraction and change detection to reachtheir full potential. The Mars Global Surveyorspacecraft and its MOC (2- to 10-m/pixel visi-ble light camera), MOLA (laser altimeter), andTES (thermal emission spectrometer) instru-ments, along with three new spectrometers(THEMIS, GRS, and MARIE) to arrive shortlyaboard the 2001 Mars Odyssey spacecraft, arenow providing comprehensive views of anotherplanet at high data volume, which will alsostretch human analytic capabilities.

Step 1: Observe and explore interestingphenomena. Visualization and exploration ofhigh-dimensional vector data are the focus ofmuch current ML research. For example, onepromising class of approaches involves dimen-sionality reduction: reducing data from manyoriginal dimensions (e.g., thousands of geneexpression measurements) to just a few dimen-sions in some new space (e.g., two or threedimensions, for easy visualization on computerdisplays). This includes classic methods, suchas principal component analysis (PCA) andmultidimensional scaling, as well as manypromising new ones, such as kernel principalcomponent analysis (5), independent compo-nent analysis (ICA) (6), and locally linear em-bedding (7). The wide variety of new dimen-sionality reduction and visualization methods,along with general trends toward ever largerand higher dimensional data sets, suggests thatthis area of ML will continue to grow in relativeimportance. This wide variety, coupled withvarying domain specificity, close collaborationbetween scientists and ML researchers.

In bioinformatics, sequence and microarraymRNA gene expression data are ramping up involume as instrumentation improves, and thescientific understanding of many fundamentallife processes is at stake. Data vector clusteringmethods have played a historic role in the earlyanalyses of microarray data (4 ). They have alsobeen used to develop new molecular classifiersof cancer types (9). Cutting-edge classificationalgorithms such as support vector machineshave been used to predict gene function anno-tations from expression data (10). Frustratingly,we do not really understand the mathematicalmapping of such cluster structures to causalmodels of underlying gene expression circuitry[but see (11)]. Another biological example is inneuroscience, where functional magnetic reso-nance imaging brain images have been decom-posed into meaningful, statistically independentcomponents of localized activity with the ICAmethod (12).

A good example of how ML classifiers caninform an overall scientific effort, even whenthose classifiers are not based on scientific the-ories per se, is the NASA work on learningdetectors of planetary features such as volca-noes and impact craters. The scientific need forgeological feature catalogs has led to multiyear

human surveys of Mars orbital imagery yield-ing tens of thousands of cataloged, character-ized features including impact craters, faults,and ridges. If the tedious aspects of this workcould be vastly accelerated and made objectiveby automation, then feature relationships andimpact crater counts could be used to order anddate geological units with much finer spatialand temporal resolution than is now possible. Infact, the stratigraphic record could be objective-ly reanalyzed at high resolution. Recent stepstoward this goal involve learning and patternrecognition. Trainable classifiers for geomor-phological features were initially based on sim-ple Gaussian models for orbital image data (13)and later improved with PCA (14 ) and supportvector machines (15 ). So far the most accuratefeature detector models bare little resemblanceto the process models describing the formationof those geological features.

Simulation observations are also a fruitfulbut largely untapped source of data for MLtechniques. For example, high-quality particlesimulators of planetary and comet formations[e.g., (16)] generate vast amounts of data, forwhich careful manual examination is usuallyinfeasible. Semiautomated exploration of thisdata, such as detecting outlier behaviors signif-icantly and interestingly different from previoussimulation runs, could help guide scientific in-vestigation and drastically improve overallthroughput for such increasingly important“science by simulation” work.

Step 2: Generate hypotheses. Many data-clustering algorithms may be treated as fittingvector data to a mixture of simpler probabilitydistributions such as Gaussians, one per cluster.Figure 1 shows an example of a muscle clusterof 81 genes culled from hierarchical Expecta-tion Maximization clustering analysis [as in(11)] of a C2C12 mouse muscle differentiationtime course microarray, involving about 9000genes (13). This cluster was hand-picked bybrowsing a cluster hierarchy at the top level topick out genes up-regulated over the timecourse and at the second level to pick out geneswhose pattern of induction was tightly correlat-ed. Biological inspection of this gene list re-veals known transcriptional regulators of mus-cle differentiation as well as their downstreamtarget genes, which include classic markers ofmuscle differentiation. Clones for which nofunction could be attributed (of which therewere 15) become candidates for a role in regu-lation or execution of muscle differentiation.On the basis of the assumption that genes thatfall into the same expression cluster are partic-ularly likely to share function, this provides abasis for guiding model formulation for geneswith no known function. Thus, such clusteringprovides a basis for bootstrapping the processof understanding possible functions of poorlyunderstood genes from better understood ones.

From unsupervised learning methods suchas clustering or dimensionality reduction, thereemerge patterns in data that demand explana-

Fig. 1. Example of red-green activity display of one muscle gene cluster. The x axis indicates thestage of muscle development. The y axis indicates gene number. Colors represent discretized levelof expression: Green is up-regulated, red is down-regulated, and black shows no change relative tothe expression level of a reference sample.

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tion. In expression bioinformatics, one observesclusters of coexpressed genes, which may sug-gest hypotheses of direct or indirect coregula-tion. Observed data may be also fit numericallyto a relatively generic but predictive, causalmodel, such as a fully recurrent analog neuralnetwork model for gene expression data. Thishas been done for morphogenetic gene regula-tion networks in the early Drosophila melano-gaster embryo, resulting in predictive models(18). From these learned models, specific hy-potheses were derived about which gap genecontrols each boundary of each modeled stripeof even-skipped gene expression. This kind ofmodel inference falls into the fundamental MLcategory of nonlinear regression. It is still large-ly up to the imagination of the human scientistto transform observed patterns into testable hy-potheses, but the model provides a mathemati-cal language for doing so. Thus, model inver-sion methods, like unsupervised learning meth-ods, have the potential to formalize or automatesome aspects of hypothesis generation, particu-larly if coupled with Bayesian inference to en-sure that the inverse problem is well posed.

There are extreme cases in which the auto-mation of hypothesis generation is especiallyimportant. In robotic planetary exploration,speed-of-light communication delays and band-width limitations make robotic autonomy valu-able or essential. Future reconnaissance geolo-gy robots such as Mars rovers would benefitfrom the use of supervised and unsupervisedlearning to classify visible rock surfaces. Theywould also benefit from having a comprehen-

sive library of preprogrammed geological mod-els and the ability to tune, instantiate, or recom-bine them to fit locally available evidence.These capabilities could be used to autono-mously acquire and send back the most signif-icant data available at each site. The in situspacecraft, like a human field geologist, couldmaintain multiple working hypotheses and lookfor discriminating observations. Very earlysteps in this direction are taken in (19) and (20).Similar requirements for autonomy will applyto solar system orbital missions, in which on-board analysis of survey observations may sug-gest detailed follow-up observations. An earlyopportunity to test on-board science software inEarth orbit, with many extraterrestrial analogs,may arise with the Autonomous SciencecraftConstellation experiment (21).

Step 3: Formulate model to explain phe-nomena. Learning good models from data is acentral goal of ML, so the field offers a widevariety of powerful tools for this critical stage.Mixture models for clustering and recurrentanalog neural net models for nonlinear regres-sion have effective parameter-inference algo-rithms as described above (for step 2). Likeunsupervised data-clustering algorithms, super-vised learning algorithms have their own,equally generic statistical interpretations. At theother extreme in model specificity stand de-tailed simulatable mathematical models of par-ticular systems, as practiced in computationalphysics, chemistry, and more recently compu-tational biology. An important direction in MLresearch is to create automatically models of

intermediate generality that can incorporatesuccessively more domain expertise. One ex-ample is the method of trainable Markov Ran-dom Fields (MRFs), which have been appliedto images of solar active regions with imageryfrom the Michelson Doppler Imager (MDI)instrument aboard the Solar and HelioseismicObservatory (SOHO) spacecraft (22, 23) (seeFig. 2). Also of intermediate generality is theinfluential Bayes Net or “graphical model” for-malism to describe interacting causal chains ofhidden and observable stochastic variables(24 ). These models generalize Hidden Markovmodels. Frontier research in these areas ad-dresses the inference of graphical model struc-ture (connections between variables) and prob-ability distribution parameters by optimizationfrom data [e.g., (25, 26)]. Future research willhave to address the problem of variable, data-dependent graph structure such as arises in bi-ological development, fluid physics, or the rep-resentation of abstract networks of interrelatedhypotheses and concepts.

When the observed data can be labeled bya scientist as “positive” and “negative” exam-ples of the phenomena of interest, supervisedclassifiers can be learned. Specific classifiermethods tend to fall into one of two groups:(i) Generative models, which strive to capturethe joint probability of the variables in thephysical system. These approaches can in-clude the learning of causal mechanisms(e.g., Bayesian networks or graphical mod-els). (ii) Discriminative models, which striveonly to capture the ability to distinguish pos-

Fig. 2. Solar image analysis. The raw (SOHO/MDI) data consist oftemporal sequences of two-dimensional images of intensity andmagnetic flux. (A) Photograms (over 6 days). (B) Magnetograms.(C) Labelings given by the learned MRF model. The model assignseach pixel to one of three classes:sunspot (deep red), faculae

( yellow), or quiet sun (cyan). Given the class labeling, a pixel’sintensity and magnetic flux are assumed to be governed by mixturemodels appropriate to that class. The mixture model parameters foreach class are learned. Images courtesy of M. Turmon, K. Shelton, andthe MDI team.



itive from negative examples, while avoidingthe expense of learning full joint probabilitydensities. Support vector machines (SVMs)(27 ) are a very popular and successful exam-ple of this group.

As an example of discriminative modelsfor robotic geology, Gilmore et al. (19) dem-onstrate the ability to discriminate carbonatefrom noncarbonate minerals using a feed-forward neural network taking inputs from areflectance spectrometer suitable for use on aMars rover.

Discriminative models make no attempt toexplicitly capture the true underlying physics ofthe phenomena. Nevertheless, as many recentsuccessful applications of methods such asSVMs have shown [e.g., (10 )], such classifierscan provide strong insights into the nature ofthe phenomena, including such aspects aswhich input dimensions are most useful, whichexamples are most likely to be outliers, andwhat new observations might be most worth-while to gather (e.g., “active learning” methodsdiscussed in the next section).

It is important to realize that even the rela-tively more complex generative/probabilisticmodels need not be realistic to be useful. This isa critical point for potential scientific users ofML technologies to realize and try to exploit inpractice. For example, one could train ML clas-sifiers to classify atmospheric image data pixels(e.g., “clouds” versus “nonclouds”), using asinputs not only the raw pixel values but also theprediction of complex realistic physics-basedmodels for each pixel. In this way, modern MLmethodologies can systematically determine onwhat sort of images the complex models actu-ally work or fail. Furthermore, active learningmethods can suggest new data that would allowthe ML classifier to best correct the predictionscoming from the realistic model, toward muchmore accurate overall classifications.

Step 4: Test predictions made by the theory.One of the most expensive and error-proneaspects of ML of classifier models is the needfor relatively large volumes of (manually) la-beled data. An emerging topic in current MLresearch is active learning, which provides au-tomated means of determining which potentialnew data points would be most useful to label.Thus, active learning methods directly addressthe issue of automating the process of determin-ing what predictions to make and what data togather to test them. Active learning promisesgreat savings over the more standard [e.g., (13)]use of automated classifiers in the scientificprocess, by radically reducing the amount ofmanual labeling required from scientists and labtechnicians.

A good example of this emerging area ofML, which also illustrates some of the uniqueadvantages of the discriminative SVM ap-proach, is given by Tong and Koeller (28). Acommon active learning technique is to selectas the next data point to label (e.g., by per-

forming an experiment) a point for which thecurrent ML model is least certain of its clas-sification. However, Tong and Koeller (28)show that selecting the new data point thatwould most evenly halve the number of mod-els consistent with the augmented data set,regardless of whether the label turns out to bepositive or negative, can be much more ef-fective. They show that the special geomet-rical nature of the “version space” of SVMmodels consistent with the data is ideallysuited to the active learning task.

Step 5: Modify theory and repeat (at step2 or 3). ML work on “theory refinement”addresses the issue of how best to updatemodels on the basis of new data. Much earlywork on neural networks focused on suchincremental, online tasks. Both ML and thetraditional scientific process approaches facethe same fundamental issues, such as when torefine the theory versus doubt the data andhow to best seek parsimony in explanation.For parsimony, Occam’s razor has a technicalcounterpart in ML theory. Following Ein-stein’s “everything should be made as simpleas possible, but not simpler,” ML theory pro-vides formal mathematic bases for encourag-ing low model complexity. In model selec-tion, one may minimize complexity metricssuch as Vapnik-Chervonenkis dimension orminimum description length (1), while main-taining good performance on a training dataset, in order to obtain good generalization totest data sets drawn from the same or relateddistributions.

Obstacles to AutomationTo achieve its promise to improve the scienceprocess across all stages, ML methods face avariety of outstanding obstacles. One is thatmost ML work to date focuses on vector data,limiting its value for richer, nonvector relationssuch as graph clusters (29) and text data. MLwork on bioinformatics and Internet data thataddresses these issues is relatively new andimmature. Similarly, most ML work assumesrelatively fixed model structures, whereas vari-able structures (such as data-dependent graphi-cal models) would often seem necessary—es-pecially during early stages of investigation,when nothing even close to a unified theory isavailable to guide the structure.

From a systems perspective, much workis still needed. Standards and methods formodel sharing and formal specification, en-abling ML methods to communicate withboth scientists and other ML methods, arestill relatively primitive and incomplete.The Holy Grail of integrating automatedreasoning across all relevant representa-tions and processes seems far from currentreality. This is in no small part due to ourcontinuing ignorance of the creative humanthought processes guiding the art of doingscience.

ConclusionsDespite the obstacles, current machine learn-ing (ML) research is producing impressiveadvances on some fundamental issues. Theseinclude scaling up ML methods to large sam-ple and dimension sizes, finding the produc-tive balance between generative and discrim-inative approaches, and focusing attention onthe most useful data (e.g., active learning).We expect steady progress on these coreareas of ML research in coming years.

Some core ML research directions areespecially likely to further the partial automa-tion of scientific processes. These includelearning from nonvector data such as labeledgraphs, text, and images; multiscale methodsfor large-scale optimization in inference; andfeature selection to find the most relevantaspects of large data sets.

A particularly exciting recent trend in MLresearch has been the development of nonlin-ear Mercer kernel versions of many classiclinear methods [e.g., kernel PCA, kernelnearest-neighbor, and kernel Fischer discrim-inates (30)] and domain-specific kernels[e.g., (31)]. The promise of such kernel meth-ods is the ability to learn highly accuratemodels on large feature spaces, while over-coming the traditional “curse of dimension-ality.” We expect breakthroughs in this area,both for new algorithms and for new power-ful domain-specific kernels (32).

Although we have focused on semiauto-mated, human-interactive scientific inference,there is already demand for more fully automat-ic inference in special situations. Such situa-tions include very short or long time scales orlocations inhospitable to human intervention,such as robotic planetary exploration missions.It will be a fascinating and instructive endeav-or—requiring contributions across technology,science, and even philosophy—to develop andunderstand the full spectrum of such scientificinference systems.

References and Notes1. T. Mitchell, Machine Learning (McGraw-Hill, New

York, 1997).2. C. Burge, S. Karlin, J. Mol. Biol. 268, 78 (1997).3. D. D. Shoemaker et al., Nature 409, 922 (2001).4. P. T. Spellman et al., Mol. Biol. Cell 9, 3273 (1998).

Data analysis discussed by M. B. Eisen, P. T. Spellman,P. O. Brown, D. Botstein, Proc. Natl. Acad. Sci. U.S.A.95, 14863 (1998).

5. B. Scholkopf, A. Smola, K.-R. Muller, in Advances inKernel Methods–SV Learning, B. Scholkopf, C. J. C.Burges, A. J. Smola, Eds. (MIT Press, Cambridge, MA,1999), pp. 327–352.

6. A. J. Bell, T. J. Sejnowski, Neural Comput. 7, 6 (1995).7. S. Roweis, L. Saul, Science 290, 2323 (2000).8. M. Shiozawa and the Super-Kamiokande collabora-

tion, Nucl. Instrum. Methods Phys. Res. Sect. A 433,240 (1999).

9. T. R. Golub et al., Science 286, 531 (1999).10. M. Brown et al., Proc. Natl. Acad. Sci. U.S.A. 97, 1

(2000).11. E. Mjolsness, T. Mann, R. Castano, B. Wold, Adv.

Neural Inform. Processing Syst.12, 928 (2000).12. T.-P. Jung et al., Proc. IEEE 89, 7 (2001).13. M. C. Burl et al., Machine Learning 30, 165 (1998).14. M. C. Burl et al., paper presented at 5th International

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Symposium on Artificial Intelligence, Robotics, andAutomation in Space (i-SAIRAS), Montreal, Canada,June 2001.

15. D. DeCoste, B. Scholkopf, Machine Learning, in press.16. H. F. Levison, L. Dones, M. J. Duncan, Astron. J. 121, 4

(2001).17. B. Williams, S. Damle, B. Wold, unpublished data.18. J. Reinitz, D. H. Sharp, Mech. Dev. 49, 133 (1995).

Mathematical foundation introduced by E. D. Mjol-sness, H. Sharp, J. Reinitz, J. Theor. Biol. 152, 429(1991).

19. M. S. Gilmore et al., J. Geophys. Res. 105, 29233(2000).

20. T. Estlin et al., in Proceedings of the American Asso-ciation for Artificial Intelligence Conference (AAAIPress/MIT Press, Orlando, FL, 1999), pp. 613–620.

21. A. Davies et al., Autonomous Sciencecraft Constella-tion Science Study Report ( Jet Propulsion Laboratory,Pasadena, CA, August 2001). Available at http://asc.jpl.nasa.gov, which also describes the technologyof the ASC experiment.

22. M. Turmon, S, Mukhtar, J. Pap, in Proceedings of theThird Conference on Knowledge Discovery and DataMining (AAAI Press, Newport Beach, CA, 1997), pp.267–270.

23. M. Turmon, J. M. Pap, S. Mukhtar, in Structure andDynamics of the Interior of the Sun and Sun-like Stars(ESA SP-418, European Space Agency PublicationsDivision, Noordwijk, Netherlands, 1998), pp. 979–984.

24. M. Jordan, Ed., Learning in Graphical Models (Kluwer,Dordrecht, Netherlands, 1998).

25. K. Murphy, Y. Weiss, M. Jordan, in Proceedings of theFifteenth Conference on Uncertainty in Artificial In-telligence, K. B. Laskey, H. Prade, Eds. (Kaufmann, SanFrancisco, CA, 1999), pp. 467–475.

26. Y. Weiss, Neural Comput. 12, 1 (2000).27. C. J. C. Burges, Data Mining Knowledge Discov. 2, 2

(1998).28. S. Tong, D. Koller, in Proceedings of the Seventeenth

International Conference on Machine Learning (Kauf-man, San Francisco, CA, 2000), pp. 999–1006.

29. S. Gold, A. Rangarajan, E. Mjolsness, Neural Comput.8, 4 (1996).

30. S. Mika, G. Ratsch, K.-R. Muller, Adv. Neural Inform.Processing Syst. 13, 591 (2001).

31. A. Zien et al., Bioinformatics 16, 799 (2000).32. Good Web sites for kernel methods are www.kernel-

machines.org/ and www.support-vector.net/. A goodsite about graphical models is www.auai.org/. Theannual Neural Information Processing Systems (NIPS)conference is a good source of the latest results inboth areas and many others (with complete onlinearchives at http://nips.djvuzone.org/). The Web sitefor the JPL Machine Learning Systems Group is www-aig.jpl.nasa.gov/mls.

33. Work supported in part by the Intelligent Data Under-standing and Automated Reasoning elements of theNASA Intelligent Systems program, by the WhittierFoundation, by the NASA Applied Information SystemsResearch Program, by a National Research ServiceAward from the NIH, and by the NASA Autonomy andCross Enterprise Technology Development Programs.



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