Tide-predicting Machines

Predicting Bankruptcy with Support Vector Machines

Predicting Disk Scheduling Performance with Virtual Machines

A method for predicting the performance of disk scheduling algorithms on real machines using only their performance on virtual machines is suggested. The method uses a dynamically loaded kernel intercept probe (iprobe) to adjust low-level virtual device timing to match that of a simple model derived from the real device. An example is provided in which the performance of a newly proposed disk s...

Predicting Translation Performance with Referential Translation Machines

Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the 3rd system results for German to English sentence-level prediction of translation quality and the 2nd system results according to root mean squared error. In addition to the new features about substring distan...

Referential Translation Machines for Predicting Translation Performance

Referential translation machines (RTMs) pioneer a language independent approach for predicting translation performance and to all similarity tasks with top performance in both bilingual and monolingual settings and remove the need to access any task or domain specific information or resource. RTMs achieve to become 1st in documentlevel, 4th system at sentence-level according to mean absolute er...

Predicting Time Series with Support Vector Machines

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) an insensitive loss and (ii) Huber's robust loss function and discuss how t o c hoose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform...