ACORA: Distribution-Based Aggregation for Relational Learning from Identifier Attributes

Feature construction through aggregation plays an essential role in modeling relational domains with one-to-many relationships between tables. One-to-many relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation from categorical attributes that can take many values (e.g., object identifiers). We present a novel aggregation method as part of a relational learning system ACORA, that combines the use of vector distance and meta-data about the class-conditional distributions of attribute values. We provide a theoretical foundation for this approach deriving a "relational fixed-effect" model within a Bayesian framework, and discuss the implications of identifier aggregation on the expressive power of the induced model. One advantage of using identifier attributes is the circumvention of limitations caused either by missing/unobserved object properties or by independence assumptions. Finally, we show empirically that the novel aggregators can generalize in the presence of identi- fier (and other high-dimensional) attributes, and also explore the limitations of the applicability of the methods.Information Systems Working Papers Serie

Quantum Computation Based on Retarded and Advanced Propagation

Computation is currently seen as a forward propagator that evolves (retards) a completely defined initial vector into a corresponding final vector. Initial and final vectors map the (logical) input and output of a reversible Boolean network respectively, whereas forward propagation maps a one-way propagation of logical implication, from input to output. Conversely, hard NP-complete problems are characterized by a two-way propagation of logical implication from input to output and vice versa, given that both are partly defined from the beginning. Logical implication can be propagated forward and backward in a computation by constructing the gate array corresponding to the entire reversible Boolean network and constraining output bits as well as input bits. The possibility of modeling the physical process undergone by such a network by using a retarded and advanced in time propagation scheme is investigated. PACS numbers: 89.70.+c, 02.50.-r, 03.65.-w, 89.80.+hComment: Reference of particle statistics to computation speed up better formalized after referee's suggestions. Modified: second half of Section I, Section IIC after eq.(7), Section IID and E. Figure unchange