Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning

We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), using which we establish sharp excess risk bounds for MTL in terms of distribution- and data-dependent versions of the Local Rademacher Complexity (LRC). We also give a new bound on the LRC for norm regularized as well as strongly convex hypothesis classes, which applies not only to MTL but also to the standard i.i.d. setting. Combining both results, one can now easily derive fast-rate bounds on the excess risk for many prominent MTL methods, including---as we demonstrate---Schatten-norm, group-norm, and graph-regularized MTL. The derived bounds reflect a relationship akeen to a conservation law of asymptotic convergence rates. This very relationship allows for trading off slower rates w.r.t. the number of tasks for faster rates with respect to the number of available samples per task, when compared to the rates obtained via a traditional, global Rademacher analysis.Comment: In this version, some arguments and results (of the previous version) have been corrected, or modifie

Generic Environment for Simulating Launch Operations

GEM-FLO (A Generic Simulation Environment for Modeling Future Launch Operations) is a computer program that facilitates creation of discrete-event simulation models of ground processes in which reusable or expendable launch vehicles (RLVs) are prepared for flight. GEM-FLO includes a component, developed in Visual Basic, that generates a graphical user interface (GUI) and a component, developed in the Arena simulation language, that creates a generic discrete-event simulation model. Through the GUI, GEM-FLO elicits RLV design information from the user. The design information can include information on flight hardware elements, resources, and ground processes. GEM-FLO translates the user s responses into mathematical variables and expressions that populate the generic simulation model. The variables and expressions can represent processing times, resource capacities, status variables, and other process parameters needed to configure a simulation model that reflects the ground processing flow and requirements of a specific RLV. Upon execution of the model, GEMFLO puts out data on many measures of performance, including the flight rate, turnaround time, and utilization of resources. This information can serve as the basis for determining whether design goals can be met, and for comparing characteristics of competing RLV design

Gap-based estimation: Choosing the smoothing parameters for Probabilistic and general regression neural networks

Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by crossvalidation or clustering. In this letter, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy

Multicriteria Design Of Manufacturing Systems Through Simulation Optimization

Simulation is a popular tool for the design and analysis of manufacturing systems. The popularity of simulation is due to its flexibility, its ability to model systems when analytical methods have failed, and its ability to model the time dynamic behavior of systems. However, in and of itself, simulation is not a design tool; therefore, in order to optimize a simulation model, it often must be used in conjunction with an optimum-seeking method. This paper describes an interactive (decision maker-computer) methodology for multiple response optimization of simulation models. This approach is based on a multiple criteria optimization technique called the STEP method. The proposed methodology is illustrated with an example involving the optimization of a manufacturing system. © 1994 IEE

Multiple response optimization of simulation models

This paper introduces a new interactive approach for optimizing multiple response simulation models. This approach is based on the Geoffrion-Dyer-Feinberg (GDF) Vector maximal algorithm. An application of this method to the simulation of an inventory model is presented

A Composite Measure Of Usability For Human-Computer Interface Designs

A methodology for formulating a composite measure of interface usability is provided. The measure integrates multiple usability criteria into a single measure by which designs can be directly compared. The primary advantages of the proposed approach are the ability to consider multiple criteria and to weight the importance of these criteria according to a particular company\u27s priorities and requirements. © 1995 Elsevier Science B.V. All rights reserved

Development Of A Methodology For The Use Of Neural Networks And Simulation Modeling In System Design

In this paper the use of metamodels to approximate the reverse of simulation models is explored. This purpose of the approach is to achieve the opposite of what a simulation model can do. That is, given a set of desired performance measures, the metamodels output a design to meet management goals. The performance of several neural network simulation metamodels was compared to the performance of a stepwise regression metamodel in terms of accuracy. It was found that in most cases, neural network metamodels outperform the regression metamodel. It was also found that a modular neural network performed the best in terms of minimizing the error of prediction

Critical Tools Identification And Characteristics Curves Construction In A Wafer Fabrication Facility

The purpose of this research was to identify the factors in a wafer fabrication facility that significantly affect the cycle times of two main technologies that are currently in process and in demand for the next few years. Moreover, the goal was to construct the characteristics curves that would provide information about the different capabilities of a wafer fabrication facility for several improvement scenarios. A valid simulation model of the whole production line of the fabrication facility was built. The input factors in the fab that significantly affect cycle time, were identified through factor screening experiments. Based on these factors, several scenarios involving addition of tools, were identified and the characteristics curves were constructed for each scenario. These characteristics curves were used to relate cycle time to production volume capacities