Brain-wave representation of words by superposition of a few sine waves

Data from three previous experiments were analyzed to test the hypothesis that brain waves of spoken or written words can be represented by the superposition of a few sine waves. First, we averaged the data over trials and a set of subjects, and, in one case, over experimental conditions as well. Next we applied a Fourier transform to the averaged data and selected those frequencies with high energy, in no case more than nine in number. The superpositions of these selected sine waves were taken as prototypes. The averaged unfiltered data were the test samples. The prototypes were used to classify the test samples according to a least-squares criterion of fit. The results were seven of seven correct classifications for the first experiment using only three frequencies, six of eight for the second experiment using nine frequencies, and eight of eight for the third experiment using five frequencies.

By a Committee appointed by the National Mediation Board in the matter of the application of : Air Line Pilots Association, International alleging representation dispute pursuant to Section 2, Ninth, of the Railway Labor Act and Flight Engineers' International Association involving employees of United Air Lines, Incorporated. Findings upon investigation January 17, 1961.

File no. C-2946.

A bibliography of British municipal history : including gilds and parliamentary representation / by Charles Gross.

Includes index.

Transportation of higher-level language programs: exemplified by an ALGOL 68 transportation representation /

Includes bibliographical references.

Parliamentary representation; further return to an address to His Majesty, dated 12 December, 1831; for copies of instructions given by the Secretary of State for the Home department with reference to Parliamentary representation; likewise copies of letters of reports received by the Secretary of state for the Home department in answer to such instructions. Reports from Commissioners on proposed division of counties and boundaries of boroughs ...

At bottom of t.p.: 141.

A general representation of reveal'd religion : in which the chief prejudices that have been entertain'd against it, are examin'd; and the practical end propos'd by it, recommended, and rescued from unfaithful evasions /

Head- and tailpieces, initials.

Induction by a Hilbert hypercube representation

This thesis describes a novel connectionist machine utilizing induction by a Hilbert hypercube representation. This representation offers a number of distinct advantages which are described. We construct a theoretical and practical learning machine which lies in an area of overlap between three disciplines - neural nets, machine learning and knowledge acquisition - hence it is refered to as a "coalesced" machine. To this unifying aspect is added the various advantages of its orthogonal lattice structure as against less structured nets. We discuss the case for such a fundamental and low level empirical learning tool and the assumptions behind the machine are clearly outlined. Our theory of an orthogonal lattice structure the Hilbert hypercube of an n-dimensional space using a complemented distributed lattice as a basis for supervised learning is derived from first principles on clearly laid out scientific principles. The resulting "subhypercube theory" was implemented in a development machine which was then used to test the theoretical predictions again under strict scientific guidelines. The scope, advantages and limitations of this machine were tested in a series of experiments. Novel and seminal properties of the machine include: the "metrical", deterministic and global nature of its search; complete convergence invariably producing minimum polynomial solutions for both disjuncts and conjuncts even with moderate levels of noise present; a learning engine which is mathematically analysable in depth based upon the "complexity range" of the function concerned; a strong bias towards the simplest possible globally (rather than locally) derived "balanced" explanation of the data; the ability to cope with variables in the network; and new ways of reducing the exponential explosion. Performance issues were addressed and comparative studies with other learning machines indicates that our novel approach has definite value and should be further researched.