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An Advanced Gradient Descent

Abstract Number:

3858 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Mian Adnan (1)

Institutions:

(1) Bowling Green State University, N/A

First Author:

Mian Adnan  
Bowling Green State University

Presenting Author:

Mian Adnan  
Bowling Green State University

Abstract Text:

The Traditional Gradient Descent may not provide the proper estimate of the parameter because of the problem of the presence of several local optima for the optimum points for the unknown surface function of the data over entire grid. Attempt has been made here to demonstrate a more successful approach that estimates the global parameter.

Keywords:

Gradient Descent|Local and Global Optima| | | |

Sponsors:

Section on Statistical Learning and Data Science

Tracks:

Miscellaneous

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I have read and understand that JSM participants must abide by the Participant Guidelines.

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I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

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