A survey of induction algorithms for machine learning

Central to all systems for machine learning from examples is an induction algorithm. The purpose of the algorithm is to generalize from a finite set of training examples a description consistent with the examples seen, and, hopefully, with the potentially infinite set of examples not seen. This paper surveys four machine learning induction algorithms. The knowledge representation schemes and a PDL description of algorithm control are emphasized. System characteristics that are peculiar to a domain of application are de-emphasized. Finally, a comparative summary of the learning algorithms is presented

Invariant tensors and the cyclic sieving phenomenon

We construct a large class of examples of the cyclic sieving phenomenon by expoiting the representation theory of semi-simple Lie algebras. Let $M$ be a finite dimensional representation of a semi-simple Lie algebra and let $B$ be the associated Kashiwara crystal. For $r\ge 0$, the triple $(X,c,P)$ which exhibits the cyclic sieving phenomenon is constructed as follows: the set $X$ is the set of isolated vertices in the crystal $\otimes^rB$; the map $c\colon X\rightarrow X$ is a generalisation of promotion acting on standard tableaux of rectangular shape and the polynomial $P$ is the fake degree of the Frobenius character of a representation of $\mathfrak{S}_r$ related to the natural action of $\mathfrak{S}_r$ on the subspace of invariant tensors in $\otimes^rM$. Taking $M$ to be the defining representation of $\mathrm{SL}(n)$ gives the cyclic sieving phenomenon for rectangular tableaux

The Perfect Crime? FIFA and the Absence of Accountability in Switzerland

Article published in the Md. J. International Law

Data Processing In The Texas A & M University Library

The Texas A & M University Library embraced automation as a way of life when it became the first library in the Southwest to employ a Data Processing Supervisor as a full-time Library staff member in September, 1964. The creation of such a position as part of the Library staff was only one of several favorable circumstances which combined to provide the necessary foundation for the achievements outlined in this paper. In addition to an enthusiastic University administration which provided requested supplemental funds for a special conversion project, the Library has access to the University's centralized data processing facility, which is one of the largest such University installations in the Southwest. The Data Processing Center houses an IBM 7094-1401 computer system with 14 magnetic tape drives, two separate off-line 1401 tape systems (one with a 1404 printer), and a battery of high speed sorters, collators, and card punches. This tremendous hardware capability has proved to be a great asset to our automation program.published or submitted for publicatio