• Corpus ID: 212628314

Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification

  title={Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification},
  author={Rui Zhou},
  • Rui Zhou
  • Published 6 March 2020
  • Computer Science
  • ArXiv
In large-scale classification problems, the data set may be faced with frequent updates, e.g., a small ratio of data is added to or removed from the original data set. In this case, incremental learning, which updates an existing classifier by explicitly modeling the data modification, is more efficient than retraining a new classifier from scratch. Conventional incremental learning algorithms try to solve the problem exactly. However, for some tasks, we are only interested in the lower and… 

Figures and Tables from this paper


Quick Sensitivity Analysis for Incremental Data Modification and Its Application to Leave-one-out CV in Linear Classification Problems
This paper introduces a novel sensitivity analysis framework that can quickly provide a lower and an upper bounds of a quantity on the unknown updated classifier and demonstrates that the bounds provided by the framework are often sufficiently tight for making desired inferences.
Consistency of support vector machines and other regularized kernel classifiers
  • Ingo Steinwart
  • Computer Science
    IEEE Transactions on Information Theory
  • 2005
It is shown that various classifiers that are based on minimization of a regularized risk are universally consistent, i.e., they can asymptotically learn in every classification task. The role of the
The Regression Analysis of Binary Sequences
New Incremental Learning Algorithm With Support Vector Machines
The experimental results indicate that the MR-ISVM algorithm has not only smaller misclassification rates and sparser of the obtained classifiers, but also less total time of sampling and training compared to ISVM based on randomly independent sampling.
A Generalization of Taylor's Expansion
On a Generalization of the Lagrange Function
Some mechanical aspects of a generalization with s-order derivatives of the Lagrange function are examined. The fundamental equations of a generalized Lagrangian mechanics are established
Second-order necessary and sufficient optimality conditions for infinite-dimensional programming problems
Second-order necessary and sufficient optimality conditions are given for infinite-dimensional programming problems with constraints defined by closed convex cones. The necessary conditions are an
The influence of surface roughness on the capacitance between a sphere and a plane
The problem of capacitance between a rough sphere and a rough plane very close to each other is studied. The problem is simplified by gathering the roughness of both members onto the plane. We begin
Linear System Theory and Design
Striking a balance between theory and applications, Linear System Theory and Design, 3/e, is ideal for use in advanced undergraduate/first-year graduate courses in linear systems and multivariable system design in electrical, mechanical, chemical, and aeronautical engineering departments.
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
This paper presents a simple model for sensitivity analysis of fish population dynamics in the geosphere using Monte Carlo filtering and variance-based methods and concludes that Bayesian uncertainty estimation and global sensitivity analysis should be considered.