Open AccessProceedings Article
EfficientL 1 regularized logistic regression
Sun-In Lee,Honglak Lee,Pieter Abbeel,Andrew Y. Ng +3 more
- pp 401-408
TLDR
Theoretical results show that the proposed efficient algorithm for L1 regularized logistic regression is guaranteed to converge to the global optimum, and experiments show that it significantly outperforms standard algorithms for solving convex optimization problems.Abstract:
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many practical settings. In this paper, we propose an efficient algorithm for L1 regularized logistic regression. Our algorithm iteratively approximates the objective function by a quadratic approximation at the current point, while maintaining the L1 constraint. In each iteration, it uses the efficient LARS (Least Angle Regression) algorithm to solve the resulting L1 constrained quadratic optimization problem. Our theoretical results show that our algorithm is guaranteed to converge to the global optimum. Our experiments show that our algorithm significantly outperforms standard algorithms for solving convex optimization problems. Moreover, our algorithm outperforms four previously published algorithms that were specifically designed to solve the L1 regularized logistic regression problem.read more
Citations
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Learning in the Real World: Constraints on Cost, Space, and Privacy
TL;DR: A model for compressing the k-ne neighbor rule and the cost/time-cost trade-off, and some examples of how the model changed over time.
An Exploratory Study on Authorship Verification Models for Forensic Purpose
TL;DR: This thesis project explores extensively the possibilities of using compression features to solve the authorship verification problem and designed several innovative authorship verify models that received desirable performances and have shown potential to solve other similar problems.
A Method for Large-Scale 1-Regularized Logistic Regression
Kwangmoo Koh,Stephen Boyd +1 more
TL;DR: In this article, an efficient interior-point method for solving 1-regularized logistic regression problems with up to a thousand or so features and examples can be solved in seconds on a PC using a preconditioned conjugate gradient method.
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Painometry: wearable and objective quantification system for acute postoperative pain
Hoang Truong,Nam Bui,Zohreh Raghebi,Marta Ceko,Nhat Pham,Phuc Nguyen,Anh Nguyen,Tae-Ho Kim,Katrina Siegfried,Evan Stene,Taylor Tvrdy,Logan E. Weinman,Thomas Payne,Devin Burke,Thang N. Dinh,Sidney K. D'Mello,Farnoush Banaei-Kashani,Tor D. Wager,Pavel Goldstein,Tam Vu +19 more
TL;DR: A wearable system, named Painometry, which objectively quantifies users' pain perception based-on multiple physiological signals and facial expressions of pain, and proposes a sensing technique, called sweep impedance profiling (SIP), to capture the movement of the facial muscle corrugator supercilii, one of the important physiological expressions ofPain.
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Measures of Entropy and Change Point Analysis as Predictors of Post-Surgical Adverse Outcomes
TL;DR: The developed models did not show improvements in predictive accuracy, but they did show that change point analysis and measures of entropy and long-term memory can be useful tools in predicting postsurgical adverse outcomes.
References
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