In learning algorithm design, a direct optimization of a cost function which fits the goal is naturally desired. Particularly in recursive learning, a direct formulation for total-error-rate (TER) minimization is much desired for online classification applications. However, due to a nonlinear counting step in the classification formulation, an exact solution to minimize TER recursively is yet to be established. In this paper, we propose an exact recursive formulation for TER minimization. Based on empirical evaluations using benchmark data sets, we show that the proposed recursive classification algorithm preserves the performance of the batch mode TER while easing the computational memory load by sample based accumulation.
|Title of host publication||Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - 2017 Feb 8|
|Event||2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore|
Duration: 2016 Nov 22 → 2016 Nov 25
|Name||IEEE Region 10 Annual International Conference, Proceedings/TENCON|
|Other||2016 IEEE Region 10 Conference, TENCON 2016|
|Period||16/11/22 → 16/11/25|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering