mdh.sePublications
Change search
Refine search result
1 - 47 of 47
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Ambainis, A.
    et al.
    University of California, Berkeley, USA.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Freivalds, R.
    University of Latvia, Latvia.
    Golovkins, M.
    University of Latvia, Latvia.
    Karpinski, M.
    University of Bonn, Germany.
    Quantum finite multitape automata1999In: SOFSEM’99: Theory and Practice of Informatics: 26th Conference on Current Trends in Theory and Practice of Informatics Milovy, Czech Republic, November 27 — December 4, 1999 Proceedings, 1999, Vol. 1725, p. 340-348Conference paper (Refereed)
    Abstract [en]

    Quantum finite automata were introduced by C. Moore, J. P. Crutchfield [4], and by A. Kondacs and J. Watrous [3]. This notion is not a generalization of the deterministic finite automata. Moreover, in [3] it was proved that not all regular languages can be recognized by quantum finite automata. A. Ambainis and R. Freivalds [1] proved Chat for some languages quantum finite automats may be exponentially more concise rather than both deterministic and probabilistic finite automata. In this paper we introduce the notion of quantum finite multi-tape automata and prove that there is a language recognized by a quantum finite automaton but not by deterministic or probabilistic finite automats. This is the first result on a problem which can be solved by a quantum computer but not by a deterministic or probabilistic computer. Additionally we discover unexpected probabilistic automata recognizing complicated languages.

  • 2.
    Ambainis, A.
    et al.
    University of California, Berkeley, USA.
    Bonner, Richard
    Mälardalen University, Department of Mathematics and Physics.
    Freivalds, R.
    University of Latvia, Latvia.
    Kikuts, A.
    University of Latvia, Latvia.
    Probabilities to Accept Languages by Quantum Finite Automata1999In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 5th Annual International Conference on Computing and Combinatorics, COCOON 1999; Tokyo; Japan; 26 July 1999 through 28 July 1999; Code 151649, 1999, Vol. 1627, p. 174-183Conference paper (Refereed)
    Abstract [en]

    We construct a hierarchy of regular languages such that the current language in the hierarchy can be accepted by 1-way quantum finite automata with a probability smaller than the corresponding probability for the preceding language in the hierarchy. These probabilities converge to 1/2.

  • 3. Baborski, A.
    et al.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Managing corporate knowledge - two approaches2001In: Knowledge Acquisition and Distributed Learning in Resolving Managerial Issues, Mälardalen University Press , 2001Chapter in book (Other academic)
  • 4. Baborski, A.
    et al.
    Bonner, R.Mälardalen University, Department of Mathematics and Physics.Owoc, M.
    Knowledge Acquisition and Distributed Learning in Resolving Managerial Issues2001Collection (editor) (Other academic)
  • 5.
    Berzina, A.
    et al.
    University of Latvia, Latvia.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Ambainis-Freivalds’ algorithm for measure-once automata2001In: Fundamentals of Computation Theory: 13th International Symposium, FCT 2001 Riga, Latvia, August 22–24, 2001 Proceedings, 2001, Vol. 2183, p. 83-93Conference paper (Refereed)
    Abstract [en]

    An algorithm given by Ambainis and Freivalds [1] constructs a quantum finite automaton (QFA) withO(logp) states recognizing the language L p = a i |i is divisible by p with probability 1 − ε, for any ε > 0 and arbitrary prime p. In [4] we gave examples showing that the algorithm is applicable also to quantum automata of very limited size. However, the Ambainis-Freivalds algoritm is tailored to constructing a measure-many QFA (defined by Kondacs and Watrous [2]), which cannot be implemented on existing quantum computers. In this paper we modify the algorithm to construct ameasure-once QFA of Moore and Crutchfield [3] and give examples of parameters for this automaton. We show for the language L p that a measure-once QFA can be twice as space efficient as measure-many QFA’s.

  • 6.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    A calculus of cats2005In: 3rd International Conference on Mathematics for Engineers and Economists: Problems of Teaching and Application: Kherson, 19-23 September 2005, 2005, p. 16-19Conference paper (Other academic)
  • 7.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    A remark on the ellipticity of systems of partial differential equations1981In: Asterisque, Vol. 89-90, p. 117-128Article in journal (Refereed)
  • 8.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Calculus via limit sets2006In: Scientic Bulletin of Chelm, ISSN 2084-6770, no 2, p. 13-33Article in journal (Refereed)
  • 9.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Continuation of quasi-analytic solutions of differential systems1986In: Proc. Australian Mathematical Society Conference on Mathematical Analysis and Applications: MacQuarie University, Sydney, 1986, 1986Conference paper (Other scientific)
  • 10.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Correlations, E-R diagrams, and the problem of simultaneous econometric measurement1996In: Modelling and Control of National and Regional Economies, 1996Conference paper (Refereed)
  • 11.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Economics of information and acquisition of knowledge1998In: Acquisition of Knowledge from Databases / [ed] Baborski A., Wroclaw: University of Economics , 1998, Vol. 787, p. 193-201Chapter in book (Other academic)
  • 12.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Economics of information II: From complexity to semantics1999In: Scientific Bulletin of The Wroclaw University of Economics, Vol. 815, p. 22-31Article, review/survey (Other (popular science, discussion, etc.))
  • 13.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Elementary analysis via closure of graph2004Report (Other academic)
  • 14.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Financial systems in continuous time: modelling cash stream1996In: Modelling and Control of National and Regional Economies, 1996Conference paper (Refereed)
  • 15.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Mathematics for Information Systems: the MIST Project1995In: Proc. 6th Australasian Conference on Information Systems (ACIS'95): Curtin University of Technology, Perth, Australia, 1995, 1995, p. 546-566Conference paper (Other academic)
  • 16.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    On functional-analytic characterization of elliptic systems of PDE's with constant coefficients1981In: Functional-differential Systems: Proc. 2nd International Conference on Differential Systems, Higher College of Engineering in Zielona Gora, Poland, 1981, 1981, p. 13-15Conference paper (Other scientific)
  • 17.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Proc. Int. Workshop on Quantum Computation and Learning, May 27-29, 2000, Sundbyholm2000Conference proceedings (editor) (Refereed)
  • 18.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Basavaraj, D. K. L.
    Electronic group support systems:: Lessons from business for education1995In: Learning with Technology, ASCILITE'95: University of Melbourne, 1995, 1995, p. 43-49Conference paper (Other scientific)
  • 19.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Berry, A.
    Marjanovic, O.
    Distributed multimedia university:: From vision to reality1995In: Learning with Technology, ASCILITE'95: University of Melbourne, 1995, 1995, p. 36-42Conference paper (Other scientific)
  • 20.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Chan, A.
    Hybrid neural networks for optimal foreign exchange strategies1996In: Modelling and Control of National and Regional Economies, 1996Conference paper (Refereed)
  • 21.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Chan, A.
    Nguyen, T.
    Forecasting Australia's exchange rate:: neural network versus random walk1996In: Queensland, Australia and the Asia-Pacific Economy, 1996, p. 590-598Conference paper (Refereed)
  • 22.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Fedyszak-Koszela, A.
    Learning a real number, rationally2007Report (Other academic)
  • 23.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Freivalds, R.
    Quantum Computation and Learning: Proc. Int. Workshop, September 1999, Riga1999Conference proceedings (editor) (Refereed)
  • 24.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Freivalds, R.
    Quantum Computation and Learning: Third International Workshop, QCL 2002Riga, Latvia, Revised Proceedings2003Conference proceedings (editor) (Refereed)
  • 25.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Galant, Violetta
    Allocation of computational resource in economic search2001In: Theory of Stochastic Processes, ISSN 0321-3900, Vol. 7, no 1-2, p. 13-29Article in journal (Refereed)
  • 26.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Mamchych, T.
    Statistical literacy and contemporary higher education2006In: Contemporary Problems of Science and Education: Proc. 7th International Conference, Simeiz, June 25 - July 2, 2006., 2006, p. 166-Conference paper (Other scientific)
  • 27.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Marjanovic, O.
    Electronic collaborative classroom1995In: Learning with Technology, ASCILITE'95: University of Melbourne, 1995, 1995, p. 306-312Conference paper (Other scientific)
  • 28.
    Bonner, R.
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Sanzogni, L.
    Neural networks with product units:: The cartographic triangulation problem revisited1997In: Proc. 9th International Conference on Artificial Intelligence Applications (EXPERSYS'97): Sunderland. UK, 1997., 1997, p. 239-243Conference paper (Refereed)
  • 29.
    Bonner, R.
    et al.
    Griffith University, AUSTRALIA .
    Sanzogni, L.
    Griffith University, AUSTRALIA .
    Vaccaro, J.
    The Open University, U.K .
    Solving the triangulation problem in real time with an imbedded neural network1996In: Proc. 8th International Conference on Artificial Intelligence Applications (EXPERSYS'96): Paris, 1996, 1996, p. 75-80Conference paper (Refereed)
  • 30.
    Bonner, Richard
    Mälardalen University, Department of Mathematics and Physics.
    Getting an answer before the question: Fast adaptive information retrieval2001In: Scientific Bulletin of The Wroclaw University of Economics, Vol. 891, p. 17-27Article in journal (Refereed)
  • 31.
    Bonner, Richard
    Mälardalen University, School of Education, Culture and Communication.
    Operators that coerce the surjectivity of convolution2013Report (Other academic)
    Abstract [en]

    Considered are operators that leave the set of non-invertible (in the sense of Ehrenpreis) distributions stable. They simultaneously generalise the operation of convolution by a distribution with compact support and the operation of multiplication by a real analytic function; they are here called pseudo-convolutions since they also generalise pseudo-differential operators. (It is shown that the elliptic real analytic pseudo-differential operators leave both the non-invertible and the invertible distributions invariant.) But when the condition of real-analyticity is relaxed, such operators may map a non-invertible distribution to one invertible -- given that the invertibility in both cases concerns the same function space. By varying the space, however, one can measure the 'loss of non-invertibily' that a non-analytic perturbation may introduce. This phenomenon is here studied using the Beurling classes of functions and measuring the regularity of operator symbols in the Denjoy-Carleman sense; the Gevrey case turns out particularly simple.

  • 32.
    Bonner, Richard
    Mälardalen University, Department of Mathematics and Physics.
    Randomization of positive linear algorithms in Banach function lattices1998In: Electronic Colloquium on Computational Complexity (ECCC), 1998, p. 42-48Conference paper (Refereed)
  • 33.
    Bonner, Richard F.
    et al.
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Mamchych, Tetyana I.
    RFB Consulting, Sweden.
    Classifying Households by the (Sobolev) Norms of their Electricity Consumption2014In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 61, p. 1870-1873Article in journal (Refereed)
    Abstract [en]

    Numerical time series, but especially periodic such, are characterized up to pertinent symmetries by families of norms. The electricity consumption by a household, recorded daily during a month’s time, say, may then be encoded in a sequence of numbers; for example, as follows: the mean daily consumption, the mean daily variation of the consumption, the variation of the variation, the variation of the variation of the variation, etc. Now, replacing each of these numbers by the digits 0, 1, or 2, to say that a number is “low”, “medium”, or “high”, in relation to a collection of households, one naturally partitions the collection by the strings of these three digits; the household labeled 102   has then medium daily consumption, low daily variation, but high variation of variation, etc. We generally discuss this innocent idea and examine it in three ways: by way of toy examples, through its mathematical model (in detail presented elsewhere) and by accordingly classifying some actual electricity consumption data.

  • 34.
    Bonner, Richard
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Fedyszak-Koszela, A.
    When to stop learning?: Bounding the stopping time in the PAC model2001In: Theory of Stochastic Processes, ISSN 0321-3900, Vol. 7, no 23, p. 5-12Article in journal (Refereed)
  • 35.
    Bonner, Richard
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Freivalds, R.
    University of Latvia, Riga, Latvia.
    A survey of quantum learning2003In: Quantum Computation and Learning: Proc. Int. Workshop, May 2002, Riga, Latvia, 2003, p. 106-119Conference paper (Other academic)
    Abstract [en]

    We survey papers on problems of learning by quantum computers.The quest of quantum learning, as that of quantum computation,is to produce tractable quantum algorithms in situations, where tractableclassical algorithms do not exist, or are not known to exist. We see essentiallythree papers [18, 92, 93], which in this sense separate quantumand classical learning. We also briefly sample papers on quantum search,quantum neural processing, and quantum games, where quantum learningproblems are likely to appear.

  • 36.
    Bonner, Richard
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Freivalds, R.
    University of Latvia, Latvia.
    Kravtsev, M.
    University of Latvia, Latvia.
    Quantum versus probabilistic one-way finite automata with counter2001In: SOFSEM 2001: Theory and Practice of Informatics: 28th Conference on Current Trends in Theory and Practice of Informatics Piešt’any, Slovak Republic, November 24 – December 1, 2001 Proceedings, 2001, Vol. 2234, p. 181-190Conference paper (Refereed)
  • 37.
    Bonner, Richard
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Freivalds, R.
    University of Latvia, Riga, Latvia.
    Lapins, J.
    University of Latvia, Riga, Latvia.
    Lukjanska, A.
    University of Latvia, Riga, Latvia.
    Nonstochastic languages as projections of 2-tape quasideterministic languages1998In: Mathematical Foundations of Computer Science 1998: 23rd International Symposium, MFCS'98 Brno, Czech Republic, August 24–28, 1998 Proceedings, 1998, Vol. 1450, p. 213-219Conference paper (Refereed)
    Abstract [en]

    A language L(n) of n-tuples of words which is recognized by a n-tape rational finite-probabilistic automaton with probability 1-ε, for arbitrary ε > 0, is called quasideterministic. It is proved in [Fr 81], that each rational stochastic language is a projection of a quasideterministic language L(n) of n-tuples of words. Had projections of quasideterministic languages on one tape always been rational stochastic languages, we would have a good characterization of the class of the rational stochastic languages. However we prove the opposite in this paper. A two-tapequasideterministic language exists, the projection of which on the first tape is a nonstochastic language.

  • 38.
    Bonner, Richard
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Galant, Violetta
    Encoding knowledge in tree structures: To grow or to build? Incremental versus spiral algorithms2000In: Scientific Bulletin of The Wroclaw University of Economics, Vol. 850, p. 51-57Article in journal (Refereed)
  • 39.
    Bonner, Richard
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Mamchych, T.
    Can One Learn Too Much for One's Own Good?: Rational choice, learning, and their interplay2005In: Scientific Bulletin of The Wroclaw University of Economics, no 1064, p. 353-366Article in journal (Refereed)
    Abstract [en]

    Authors have considered a learning problem, which occurs when changes in the knowledge system of a firm (learning) alter its business objectives (preference). Grounds for evaluating learning may become known only after the learning. The article presents a review of current learning theories and the rational choice.

  • 40.
    Bonner, Richard
    et al.
    Mälardalen University, School of Education, Culture and Communication.
    Mamchych, T.
    Malchuk, I.
    On the problem of mining the Web – for a curriculum2008Conference paper (Refereed)
  • 41.
    Bonner, Richard
    et al.
    Mälardalen University, School of Education, Culture and Communication.
    Mamchych, Tetyana I.
    Speaking of the intellect, instinctively2008Conference paper (Refereed)
    Abstract [en]

    To the extent a cognitive artifact extends natural language, questions of the former should be preceded by answers to those of the latter; and, questions about cognitive science and pertinent technology should begin by asking how one may verbalise one’s ideas about cognition, one’s own cognition to start with. One does that, it is plain, in two ways: one talks of one’s thoughts and one’s feelings. One thus sees oneself not as one but as at least two. Not to cause unrest, however, one continues to talk of oneself as one, calling one’s pluralistic faculties in the singular as the Soul or the Intellect, the nest of the classical trivium of the beautiful, the good, and the intelligent. Degraded to tangible by social demand, the Intellect becomes intelligence plain, semantically rooted in behavior, hence operational, prerogative of machine. One’s remaining spiritual faculties, collectively labeled psyche, are something to aid by therapy or drugs to keep one from acting strange. Bottled in rational formaldehyde for almost a century, only recently get they restituted by science as key actors of cognition. But in public space they remain non gratae, increasingly so indeed as the digital strait jacket steadily tightens around people’s souls, taking the spark out of their social and professional presence, the spark that survived both Descartes and Marx. A century after Freud, four after Shakespeare, and four and twenty after Plato, feelings remain a mystery eluding words. Perhaps they should? We share our feelings on these vital mushy matters, irrespectively

  • 42.
    Bonner, Rikard
    et al.
    Mälardalen University, Department of Mathematics and Physics.
    Galant, V.
    Owoc, M.
    Features of Decision Trees as a Technique of Knowledge Modelling1999Conference paper (Refereed)
  • 43. Chan, A.
    et al.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Treatment of data in the development of neural networks supporting financial decisions1995In: Pan Pacific Conference on Information Systems (PACIS'95): Singapore, 1995, 1995Conference paper (Refereed)
  • 44.
    Freivalds, R.
    et al.
    Latvian State Univ.
    Bonner, Richard
    Mälardalen University, Department of Mathematics and Physics.
    Quantum inductive inference by finite automata2008In: Theoretical Computer Science, ISSN 0304-3975, E-ISSN 1879-2294, Vol. 397, no 1-3, p. 70-76Article in journal (Refereed)
    Abstract [en]

    Freivalds and Smith [R. Freivalds, C.H. Smith Memory limited inductive inference machines, Springer Lecture Notes in Computer Science 621 (1992) 19-29] proved that probabilistic limited memory inductive inference machines can learn with probability 1 certain classes of total recursive functions, which cannot be learned by deterministic limited memory inductive inference machines. We introduce quantum limited memory inductive inference machines as quantum finite automata acting as inductive inference machines. These machines, we show, can learn classes of total recursive functions not learnable by any deterministic, nor even by probabilistic, limited memory inductive inference machines.

  • 45.
    Sanzogni, L.
    et al.
    Griffith Univ, Nathan, Australia.
    Bonner, Richard
    Griffith Univ, Nathan, Australia.
    Chan, R.
    Griffith Univ, Nathan, Australia.
    Vaccaro, J.A.
    Griffith Univ, Nathan, Australia.
    Perceptrons with polynomial post-processing1996In: Proceedings of the International Conference on Tools with Artificial Intelligence, 1996, p. 472-474Conference paper (Refereed)
    Abstract [en]

    Introduces tensor-product neural networks, composed of a layer of univariate neurons followed by a net of polynomial post-processing. We look at the general approximation problem by these networks observing in particular their relationship to the Stone-Weierstrass theorem for uniform function algebras. The implementation of the post-processing as a two-layer network with logarithmic and exponential neurons leads to potentially important 'generalised' product networks which, however, require a complex approximation theory of the Müntz-Szasz-Ehrenpreis type. A backpropagation algorithm for product networks is presented and used in three computational experiments. In particular, approximation by a sigmoid product network is compared to that of a single-layer radial basis network and a multiple-layer sigmoid network.

  • 46.
    Stone, L.
    et al.
    Yigal Allon Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, Israel.
    Bonner, R.
    Griffith University, Queensland, Australia .
    Berman, T.
    Yigal Allon Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, Israel.
    Barry, S.
    Griffith University, Queensland, Australia .
    Weeks, S.
    Griffith University, Queensland, Australia .
    Lake Kinneret: A seasonal model for carbon flux through planktonic biota1993In: Limnology and Oceanography, ISSN 0024-3590, Vol. 38, no 8, p. 1680-1695Article in journal (Refereed)
  • 47.
    Vaccaro, J.
    et al.
    Griffith University, Australia.
    Bonner, R.
    Mälardalen University, Department of Mathematics and Physics.
    Pegg-Barnett phase operators of infinite rank1995In: Physics Letters A, ISSN 0375-9601, Vol. 198, p. 167-174Article in journal (Refereed)
1 - 47 of 47
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf