Extreme learning machine Cox model for high‐dimensional survival analysis
نویسندگان
چکیده
منابع مشابه
Machine Learning for Survival Analysis: A Survey
Survival analysis is a subfield of statistics where the goal is to analyze and model the data where the outcome is the time until the occurrence of an event of interest. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a...
متن کاملSequential extreme learning machine incorporating survival error potential
A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error...
متن کاملExtreme Learning Machine
Slow speed of feedforward neural networks has been hampering their growth for past decades. Unlike traditional algorithms extreme learning machine (ELM) [5][6] for single hidden layer feedforward network (SLFN) chooses input weight and hidden biases randomly and determines the output weight through linear algebraic manipulations. We propose ELM as an auto associative neural network (AANN) and i...
متن کاملRegularized Extreme Learning Machine for Large-scale Media Content Analysis
In this paper, we propose a new regularization approach for Extreme Learning Machine-based Singlehidden Layer Feedforward Neural network training. We show that the proposed regularizer is able to weight the dimensions of the ELM space according to the importance of the network’s hidden layer weights, without imposing additional computational and memory costs in the network learning process. Thi...
متن کاملApplication of Extreme Learning Machine Method for Time Series Analysis
In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics in Medicine
سال: 2019
ISSN: 0277-6715,1097-0258
DOI: 10.1002/sim.8090