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Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Updated: Dec 5, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization.

Zhi Han, Siquan Yu, Shao-Bo Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 20, 2020

Summary

This summary is machine-generated.

Deep learning models can effectively learn features without manual engineering. This study quantifies the relationship between feature learning and network depth, proving optimal generalization performance for deep neural networks.

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Deep learning excels at automatic feature extraction for pattern recognition.
Understanding the role of network depth in feature representation is a key challenge.

Purpose of the Study:

To quantify the correspondence between features and network depth in deep neural networks.
To investigate the necessity of depth for effective feature extraction and generalization.

Main Methods:

Analyzing the adaptivity of features to network depths and vice-versa.
Demonstrating a depth-parameter trade-off for single and composite feature extraction.
Proving theoretical results based on empirical risk minimization.

Main Results:

Established a quantifiable relationship between feature representation and deep neural network depth.
Showcased a trade-off between network depth and parameters for feature extraction.
Validated theoretical findings through simulations and real-world seismic prediction.

Conclusions:

Deep learning's depth is crucial for optimal feature representation and generalization.
Empirical risk minimization achieves optimal generalization in deep nets.
The findings have implications for various machine learning applications, including seismic prediction.