Random Forest with Self-Paced Bootstrap Learning in Lung Cancer Prognosis
نویسندگان
چکیده
منابع مشابه
Self-Paced Learning with Diversity
Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training. Existing methods are limited in that they ignore an important aspect in learning: diversity. To incorporate this information, we propose an approach called self-paced learning with diversity (SPLD) which f...
متن کاملSelf-Paced Curriculum Learning
Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes. In CL, the curriculum is predetermined by prior knowledge, and rema...
متن کاملSelf-Paced Multitask Learning with Shared Knowledge
This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning. We develop the mathematical foundation for the approach based on iterative selection of the most appropriate task, learning the ...
متن کاملSupplementary Materials: Self-Paced Learning with Diversity
This is the supplementary material for the paper entitled “Self-Paced Learning with Diversity”. The material is organized as follows: Section 1 gives the proof of Theorem 1. Section 2.1 and Section 2.2 present the detailed experimental settings and results on the MED (Multimedia Event Detection) dataset. Section 2.3 and Section 2.4 present the settings and detailed results on the Hollywood2 and...
متن کاملSelf-Paced Multi-Task Learning
Multi-task learning is a paradigm, where multiple tasks are jointly learnt. Previous multi-task learning models usually treat all tasks and instances per task equally during learning. Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we propose a novel multi-task learning framework that attempts to learn the tasks by simultaneo...
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: ACM Transactions on Multimedia Computing, Communications, and Applications
سال: 2020
ISSN: 1551-6857,1551-6865
DOI: 10.1145/3345314