62608981
On the delusiveness of adopting a common space for modeling IR objects: are queries documents?
Many authors, who adopt the vector space model, take the view that documents, terms, queries, etc., are all elements within the same (conceptual) space. This view seems to be a natural one, given that documents and queries have the same vector notation. We show, however, that the structure of the query space can be very different from that of the document space. To this end, concepts like preference, similarity, term independence, and linearity, both in the document space and in the query space, are discussed. Our conclusion is that a more realistic and complete view of IR is obtained if we do not consider documents and queries to be elements of the same space. This conclusion implies that certain restrictions usually applied in the design of an IR system are obviated. For example, the retrieval function need not be restricted to the ones that have the possibility to be interpreted as a similarity measure. © 1993 John Wiley & Sons, Inc.
2292157
Information Retrieval on the World Wide Web
Effective search and retrieval are enabling technologies for realizing the full potential of the Web. The authors examine relevant issues, including methods for representing document content. They also compare available search tools and suggest methods for improving retrieval effectiveness.
1349116
Improved Diagnosability Algorithms
The concepts of the PMC and BGM self-diagnosing system models of F. P. Preparata et al. (1967) and F. Barsi et al. (1976), respectively, including the notions of fault sets, consistency, and diagnosability number, are reviewed. Two one-step diagnosability algorithms are applied, one to the PMC model and the other to the BGM model. In both models, one-step diagnosability refers to a system's ability to determine all the faulty units from single collection of test results. Using the letters n, m, and tau to denote the number of units, the number of tests, and the diagnosability number, respectively, it is shown that in the BGM model the algorithm has a complexity of O(n tau /sup 2//log tau ), and, in the PMC model, the algorithm has a complexity of O(n tau /sup 2.5/). >
5530551
Learning switch configurations
10429195
Monotone term decision lists
We introduce a new representation class of Boolean functions ? monotone term decision lists ? which combines compact representation size with tractability of essential operations. We present many properties of the class which make it an attractive alternative to traditional universal representation classes such as DNF formulas or decision trees. We study the learnability of monotone term decision lists in the exact model of equivalence and membership queries. We show that, for any constant k?0, k-term monotone decision lists are exactly and properly learnable with nO(k) membership queries in nO(k3) time. We also show that n?(k) membership queries are necessary for exact learning. In contrast, both k-term monotone decision lists (k?2) and general monotone term decision lists are not learnable with equivalence queries alone. We also show that a subclass of monotone term decision lists (disj-MDL) is learnable with equivalence and membership queries, while neither type of query alone suffices.