20188177
Simulation of human verbal learning behavior
the eoinposition is or in not an ins:atl{'e of Arnohl 8el]oel/}}el,g>s so-ealhM "twelve-tone" system of l]ltb-:ieal eompositiol]. Wrilten ill a notation which modifies a notation for finite sequentia{ proces. ses developed /)y Kenneth It. lverso~:, che Mgori:hm in easily tra::slatatfle into most machine languages which allow processing of nlphanumerh: data. The decision process involved will be fully autolnatic when vharacter-recognitio~: maehiiws :hat "read" nlusi{'a/signs ape { mployed. A limited s::pply {}[ copies of lhe t)<~qter listed t>e]ow in available for distribution : The paper contains !he algorithm and a lengthy diseussi(m of issues r(lated to the synt'~c~i(~ properties of Schoenberg's system. A s s o c i a t i o n a l (' h a r a e t e r i s t i (: s o f ~I/()i'ds in T e x t S y s l (, m I)(~volopm(u~) (' o r p. , S a n t a M()l:i<l, (lalif. [i'cp(rl('d tilt; l,auPen B. I)oyle (t"et)iuiat'y 19(i2) An iuvestigation of the ten(teney {tf' words to eo..o(!e/iP in tl[li{,s of text (articles, Imragr,aphs, sentences) is continuing, with em-l)hasis on the eharaeteristies of strongly (o-oe(~urritlg op ":.tsso-elated" words. The ultimate chief.live of lho study i8 to eonfronl, litePalure searchers with usal}le representations (if text-derived associations which earl serve at least as automatically generated thesauri and at best as a.~sociative indexes. 'I'he most eonvelfient form for representing Such associations has been fotnid to lie di:> granmmtie or map-like {1!. Tile study is currently entering a new phase, in which the effect {m associations of varying the size and compost{ion of libraries is io be observed. For ihis purpose a PhiJco 2(}00 prograui is being written by which one ean flexibly deal with an unlimited number of libraries wifi0i: arc subsets of a given ~qiiastei" l i b r a r y " ; this avoids the necessity (if preparilig itew input for each rull on It different library. Library subsets to be processed can be specified either by control card or by programmed computation [2]. Further examples of the sort of study this program wil! facilitate are : (1) Automatic separation of homographs (e.g. in internatiolnal affairs "force" has a different meaning from "force" in physics; if a physics corptts can be automatically separated from one in international affairs, then homograph separation will oeetir in this ease). (2) Promotion of suppressed associations (i.e, two words wtiieh eo-oeeur …
28048643
Generalization of an Elementary Perceiving and Memorizing Machine
The Elementary Perceiver and Memorizer (EPAM) is a context, and contains sufficient information to retrieve the model of human associative memory and the processes of image of the stimulus from the association memory when human verbal learning, written in the list-processing language necessary. IPL-V. A generalization of the EPAM model (called EPAM The complex image building processes also provide an assoIII), based on earlier experiments on EPAM 11, has been dative mechanism by means of which earlier learning can formulated and programmed. usefully be brought to bear on later learning. New stimuli arc In EPAM 111. stimulus configurations and their internal learned by associating together and modifying images of images are allowed indefinite complexity of sub-parts. Complex stimuli already learned. The model is an attempt to resolve internal images are built by associating together, in list structhe problem of how meaningful learning differs from nontures, tokens for retrieving information about the sub-parts meaningful learning, from the association memory. Empirical explorations of the model are concerned generally The concatenation of tokens by the image building process with variations in behaviour as a result of varying stimulus allows for the association of stimuli in many different contexts. and response meaningfulness. The token signifies the occurrence of a stimulus in a particular 3.ieM6HTapHan nporpaMMa jhh "H3yqaioinHx MauaeT coSbiTHe CTHMyjia b oco66hhom KOHTeKCTe, a npiiuihh" (3IIJIHM (EPAM)) npeacTaß^weTCH Moaejibio cyTCTByeT b aaHHOM naineKe aocTaToiHO HHdpopMauHH, accouHaTHßiioil nfiMfiTH h npoueccoß H3yiemm mob <rro6bi BooCpawenHe CTHMyjia moskho 6mjio Bbißec" y iciOBCKa; saiman Mo,Te.-ib narmcaHa b bh«6 si3uko, hs accouHaTHßHofl naMHTH b tom cnyqae, eoJiH sto A-1H pa3paf)OTKH CnHCKOB IPL 15. Ha OCHOBaHHH OKaHCeTCH Heo6xo«HMbiM. pe3y.ibTaTon Go.ice paiiHHx nonwTOK cihopMHyjinpoCjioiKHbie npoueccw, no3Bo^aiouiHe EPAM 111 CO3Ba.iacb ii cnporpaHMHpoßa,iacb ooo6meHHaH Moae^b naßaTb BoofipasKeHHH, Taicwe 3anaxaioT accoimaTHBEPAM jiß.inioumncHTaX Ha3bißaeMoft EPAM 111. Hiiit MexaHH3M, npn noMoiiw KOToporo npeAbiAymne B EPAM 111, mc.no noaiacTefl KOMiuieKCHbix <~thonuTH MoryT 3<h<beKTHßHbiM o6pa3oM BJiHHTb Ha cnemv.iob h BiiyTpeiiHHx hx n3oGpan<eHHH MomeT decayroiiuie. Hoßbie CTHMyjiu H3yiaioTCfl b pe3yjibTaTe npe.ie.-n.no yc.iovKHfiTbcu. OioiKHbie BiiyTpeHHHe accoiiHpoßaHHtf (tbm caMMM h Mo«H<bHKau.HH) HsyienH3oopn>KeHHn-iiMeiomHeMHoroypoßHeflHH(})opMauHH hux panbine BOo6pajKeHHft. MoaeJib npeacTaßJineT o no.tCTpyuTypectpohtch cnocoSoM accouHHpoßaHHH coCoft nonbrricy peuiHTb 3a«aqy o tom, KaKHM oopa3OM 3iia«iKon miMRKOB, npw noMomH kotopux cBe^eHHH pa3JinqaTb 3HaiaromHe CBeaenHH ot He3HaiaiomHx. o no.macTnx mo>kho BUBO«HTb H3 accouHaTHßHofl 3MnHpmiecKHe onwTbi npoßOflHmnecH Ha Mo^e^b, naMtiTii. Boo6me roßopn, hmciot c paaHUMH BH«aMH noße-3auen.ieHne iiaMeKOB npoueccoM co3«aHHH boosaeHHn, npoHCxofIHWHMH H3 pa3HOBHfIHOCTH 3HaienHfi pn>Kenmi no3Bo.ineT accou.HHpoßaTb cTHMy^u, npoHCTHMy^a h oTBeTa. cxoanmiio H3 Miioro pa3Hbix kohtckctob. HaMeK 03HaLa machine elementaire pour percevoir et mettre en mblir Information concernant les sous-parties, a partir de la moire (EPAM) est un modele de la memoire humaine d'assomemoire dissociation. ciation. et dcs process de la facon humaine d'apprendre L'enchainement dcs signes au moyen de l'image eUabor^e verbalement, 6crit en langage IPL-V. En partant dcs r«sulpermet d'associer dcs excitations dans un grand nombre de tnts de tests anteVieurs, on a dlabore' et programme" une contextes diffe>ents. Le signe montre qu'il existe une excitagcneralisation dv modele EPAM, appele EPAM 111. tion dans un contexte particulier et une information suffisante Sur EPAM 111, les configurations d'excitation et les images peut, grace ace signe, regene>er l'image de I'excitation a inteneures de ces configurations ont une complexitc indefinie partir de la memoire dissociation, quand cela est necessaire. de sous-parlies. Dcs images internes complexes sont dlaborees Les procedis complexes gui 61aborent l'image fournissent en groupant. en liste de structures, les signes destine* a retaaussi un m6canisme dissociation, permettant d'elaborer faci405 IX, 1] automata theory lement la toute premiere information a partir de la toute informations ayant un sens, de celles gui n'en ont pas. derniere. Les tests empiriques dv modele se rapportent gene>alement On decouvre de nouvelles excitations en groupant (et en aux variations de fonctionnement, celles-ci £tant le resultat modifiant) les images d'excitations deja connues. Cc modele d'excitations et de reponses variables. est un essai de r6soudre le probleme de la differentiation dcs
36371266
Some Further Notes on a Class of Skew Distribution Functions
This note takes issue with a recent criticism by Dr. B. Mandelbrot of a certain stochastic model to explain word-frequency data. Dr. Mandelbrot's principal empirical and mathematical objections to the model are shown to be unfounded. A central question is whether the basic parameter of the distributions is larger or smaller than unity. The empirical data show it is almost always very close to unity, sometimes slightly larger, sometimes smaller. Simple stochastic models can be constructed for either case, and give a special status, as a limiting case, to instances where the parameter is unity. More generally, the empirical data can be explained by two types of stochastic models as well as by models assuming efficient information coding. The three types of models are briefly characterized and compared.
17536925
Building the Teraflops/Petabytes Production Supercomputing Center
Building the Teraflops/Petabytes Production Supercomputing Center Horst D. Simon, William T. C. Kramer, and Robert F. Lucas National Energy Research Scientific Computing Center (NERSC), Mail Stop 50B-4230, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 {hdsimon, wtkramer, rflucas}@lbl.gov Abstract. In just one decade, the 1990s, supercomputer centers have undergone two fundamental transitions which require rethinking their operation and their role in high performance computing. The first transition in the early to mid-1990s resulted from a technology change in high performance computing architecture. Highly parallel distributed memory machines built from commodity parts increased the operational complexity of the supercomputer center, and required the introduction of intellectual services as equally important components of the center. The second transition is happening in the late 1990s as centers are introducing loosely coupled clusters of SMPs as their premier high performance computing platforms, while dealing with an ever-increasing volume of data. In addition, increasing network bandwidth enables new modes of use of a supercomputer center, in particular, computational grid applications. In this paper we describe what steps NERSC is taking to address these issues and stay at the leading edge of supercomputing centers. Introduction In just one decade, the 1990s, supercomputer centers have undergone two fundamental transitions which require a rethinking of the basic tenets of their operation and their role in the high performance computing (HPC) world. The first transition in the early to mid 1990s was a result of a technology change in high performance computing architecture. The introduction of highly parallel distributed memory machines built from commodity parts increased the operational complexity of the supercomputer center, and required the introduction of intellectual services as equally important components of the center. We have only recently completed this transition and developed the tools necessary to bring the revolution of the mid-1990s to a successful conclusion. Now three new developments are appearing which will again force us to step up to new challenges in supercomputer center management: (1) yet another change in the architecture of supercomputing platforms, (2) the increasing importance of managing large volumes of scientific data, and (3) the deployment of a new generation of high-speed wide-area
206581413
A Time for Talk and a Time for Silence
The computer stores mountains of information which it communicates worldwide through an enormous bandwidth. We must learn to exercise severe, intelligent selectivity in mining our data mountains, and to communicate information in ways that will inform and not bury the recipients.
206718678
A General Convergence Theorem for the Decomposition Method
The decomposition method is currently one of the major methods for solving the convex quadratic optimization problems being associated with support vector machines. Although there exist some versions of the method that are known to converge to an optimal solution, the general convergence properties of the method are not yet fully understood. In this paper, we present a variant of the decomposition method that basically converges for any convex quadratic optimization problem provided that the policy for working set selection satisfies three abstract conditions. We furthermore design a concrete policy that meets these requirements.
5542141
Applications of machine learning and rule induction
Machine learning is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from experience. Expert performance requires much domain-specific knowledge, and knowledge engineering has produced hundreds of AI expert systems that are now used regularly in industry. Machine learning aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy or efficiency by discovering and exploiting regularities in training data. The ultimate test of machine learning is its ability to produce systems that are used regularly in industry, education, and elsewhere.
31930258
How Many Missing Answers Can Be Tolerated by Query Learners?
We consider the model of exact learning using an equivalence oracle and an incomplete membership oracle. In this model, a random subset of the learners membership queries is left unanswered. Our results are as follows. First, we analyze the obvious method for coping with missing answers: search exhaustively through all possible "answer patterns" associated with the unanswered queries. Thereafter, we present two specific concept classes that are efficiently learnable using an equivalence oracle and a (completely reliable) membership oracle, but are provably not polynomially learnable if the membership oracle becomes slightly incomplete. The first class will demonstrate that the aforementioned method of exhaustively searching through all possible answer patterns cannot be substantially improved in general (despite its apparent simplicity). The second class will demonstrate that the incomplete membership oracle can be rendered useless even if it leaves only a fraction 1/poly(n) of all queries unanswered. Finally, we present a learning algorithm for monotone DNF formulas that can cope with a relatively large fraction of missing answers (more than sixty percent), but is as efficient (in terms of run-time and number of queries) as the classical algorithm whose questions are always answered reliably.