the MPC model, denoted by $extsf{MPC}(o(\\log N))$, and some standard classes concerning space complexity, namely $extsf{L}$ and $extsf{NL}$, and suggest conjectures that are robust in the sense that refuting t
5 Conclusion In this paper, we proposed a new sequential rule mining algorithm named ER- Miner (Equivalence class based sequential Rule Miner). It relies on the novel idea of searching using equivalence classes of rules having the same antecedent or con- sequent. Furthermore, it an includes a...
Parent trees are advantageous for algorithms for deciding equivalence, since the most time-consuming step, retrieval of the root of a tested node, is speeded by this data structure. Thus, to organize a set of nodes into equivalence classes, use an algorithm to put them into parent trees. Vie...
We assume that all other algorithms have implicit input BG. SignR(M , sk): On input a representative M = (Mi)i∈[ ] of equivalence class [M ]R and a secret key sk = (xi)i∈[ ] ∈ (Zp∗) , return ⊥ if Mi ∈/ G∗1 for some i ∈ [ ]. Else, choose y ←R Zp...
The first function partitions the set of possible packets into a set of forwarding equivalence classes (FECs). The second function chooses a next hop for each FEC. In conventional IP forwarding, a router considers two packets to be in the same FEC when the network prefixes of their ...
60/473,373, filed on May 23, 2003, entitled “Apparatus And Method For Large Hardware Finite State Machine With Embedded Equivalence Classes”, the content of which is incorporated herein by reference in its entirety.Claims: What is claimed is: 1. A programmable finite state machine configured...
hem algorithm as well as several other feature selection algorithms. the svm is a margin classifier that draws an optimal hyperplane in the feature vector space; this defines a boundary that maximizes the margin between data samples in different classes, therefore leading to good generalization ...
equivalence classes, which are represented by two control paths. As only pairs of control paths that have a representative are processed, surplus processing of non-relevant pairs is avoided. By using symbolic representation, the disclosed subject matter is relatively scalable over an increase in data...
“enlarges” images. Yes, once “normalized” to the same print size, smaller sensors will show more noise than their full-frame counterparts, but due to better sensor efficiency and more aggressive noise suppression algorithms, they look quite decent and more than “acceptable” for many ...
The cause of the existing algorithms\' inefficiency in rule extraction in massive data sets based onequivalence matrixwas analyzed and a new definition ofequivalence matrixand the method of division for the massive data set based on the numbers of decision classes were presented. ...