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In the paper Chen, Lin, and Schölkopf, A tutorial on nu-support vector machines.Applied Stochastic Models in Business and Industry, 21(2005), 111-136, they have experimentally shown that the two methods give similar performance.

This usually happens if you compile the code on one machine and run it on another which has incompatible libraries.Program committees PLDI15(ERC), PPOPP15, MOBILESoft15, SANER15, Mobile De Li14, ICSME-ERA14, OOPSLA14, DSSO14, PLDI14, ICSE14, Hot SWUp13, MOBS13, Hot SWUp12, ICSM-ERA12, ICSM-ERA11, RAM-SE11, ICSM-ERA10, RAM-SE10, RAM-SE09 Other CSET15: co-chair PLDI13, PLDI12: finance and sponsorship chair So Cal PLS Fall12: organizer ASPLOS11: poster chair Hot SWUp09, Hot SWUp08: co-chair ARL: Models for Enabling Continuous Reconfigurability of Secure Missions with Srikanth Krishnamurthy, and Harsha Madhyastha (UCR); a Cyber-Security Collaborative Research Alliance (CRA) with PSU, CMU, Indiana, and UC Davis.Dynamic Software Updating (DSU) is a technique for updating running software systems without incurring downtime.You need to open a command window and type to see all options. [Go Top] Q: What is the difference between "." and "*" outputed during training?"." means every 1,000 iterations (or every #data iterations is your #data is less than 1,000).In this paper, we propose an algebraic way of specifying and verifying the design of dynamic updates in the OTS/Cafe OBJ method.By verifying the design of a dynamic update, we can (1) gain a better understanding of the update, e.g., how the behavior of the running system is affected by the update, (2) identify updating points where the dynamic update can be safely applied, (3) detect potential errors, and hence (4) design a safer dynamic update.However, a challenging problem is how to design a correct dynamic update so that the system after being updated will run as expected instead of causing any inconsistencies or even crashes.The OTS/Cafe OBJ method is an effective and practical approach to specifying and verifying the design of software.This issue does not occur for nu-SVC for two-class classification. A single parameter set may not be uniformly good for all k(k-1)/2 decision functions.However, as the overall accuracy is the final consideration, one parameter set for one decision function may lead to over-fitting.


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