Online Learning: A Comprehensive Survey (1802.02871v2)

Abstract: Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time. The goal of online learning is to ensure that the online learner would make a sequence of accurate predictions (or correct decisions) given the knowledge of correct answers to previous prediction or learning tasks and possibly additional information. This is in contrast to many traditional batch learning or offline machine learning algorithms that are often designed to train a model in batch from a given collection of training data instances. This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the learning type and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) supervised online learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) unsupervised online learning where there is no feedback available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.

• The paper synthesizes extensive research on online learning, categorizing methods into supervised, limited feedback, and unsupervised approaches.
• It reviews key algorithms including Perceptron, Confidence-Weighted, and kernel-based methods to ensure scalable, real-time data processing.
• It emphasizes addressing non-stationary data and integrating neural networks to enhance adaptive model performance in evolving environments.

Online Learning: A Comprehensive Survey

This survey provides an extensive examination of online learning literature, outlining its fundamental concepts, methodologies, and applications. Online learning is characterized by the real-time updating of models with data received sequentially, contrasting with traditional batch learning. This paradigm allows for efficient handling of continuous data streams, making it particularly relevant in scenarios where rapid updates to the predictive model are necessary.

Key Concepts and Taxonomy

The paper categorizes online learning literature into three primary types based on feedback and task nature:

1. Supervised Online Learning: Where the learner has access to full feedback for every decision made. This includes first-order algorithms like Perceptron and second-order methods such as Confidence-Weighted Learning. Kernel-based extensions are also discussed to manage non-linear problems efficiently.
2. Online Learning with Limited Feedback: This refers to scenarios where partial feedback is available, necessitating strategies to balance exploration and exploitation. This category typically includes bandit problems like Multi-Armed Bandits (MAB).
3. Unsupervised Online Learning: Here, the learner deals with data streams without feedback (e.g., clustering, anomaly detection). The survey touches upon how these tasks can be approached with the online learning framework.

Numerical Results and Bold Claims

The paper does not introduce new numerical results but synthesizes existing research. It recognizes the surge in interest due to theoretical promises such as sublinear regret bounds, which assert that online learners can perform competitively with the optimal offline learners over time.

Implications and Future Directions

Theoretical implications involve enhancing understanding of regret minimization and adapting online learning to more complex real-world applications with vast streams, such as financial market analysis and recommendation systems. The practical implications stress scalability, real-time adaptation, and the ability to handle evolving data distributions.

Future challenges outlined include addressing non-stationary data streams and concept drift, integrating online learning with neural networks for deep learning applications, and ensuring robust learning in high-velocity, high-variety data environments.

Conclusion

The survey provides an authoritative guide to online learning, highlighting its potential while acknowledging areas requiring further exploration. It acts as a bridge between established concepts and innovative applications, providing a solid base for future research and development in scalable, adaptive machine learning techniques.