A perspective view and survey of meta-learning

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Machine Learning, Neural and Statistical Classification

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10. Reinforcement learning

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The Lack of A Priori Distinctions Between Learning Algorithms

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Classifier systems and genetic algorithms

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A Model of Inductive Bias Learning

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A model of inductive bias learning

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Multitask Learning

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COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS11Research reported in this paper was supported in part by the National Science Foundation under grant DCR 71-01997 and by the Horace H. Rackham School of Graduate Studies under grant 387156.

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The Need for Biases in Learning Generalizations

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Pattern Recognition: Human and Mechanical

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Cognitive systems based on adaptive algorithms

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Lifelong Learning Algorithms

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A Conservation Law for Generalization Performance

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Meta-Learning by Landmarking Various Learning Algorithms

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Error Reduction through Learning Multiple Descriptions

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Shift of bias for inductive concept learning

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Generalizing from Case Studies: A Case Study

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Explanation-based learning: a problem solving perspective

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Learning Classifier Systems, From Foundations to Applications

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On the Accuracy of Meta-learning for Scalable Data Mining

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Meta-Learning in Distributed Data Mining Systems: Issues and Approaches

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Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection

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Experiments on multistrategy learning by meta-learning

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Evaluation and Selection of Biases in Machine Learning

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Learning One More Thing

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Knowledge Discovery Via Multiple Models

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Recursive Automatic Bias Selection for Classifier Construction

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Characterization of classification algorithms

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What Is a Learning Classifier System?

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Learning and Inductive Inference

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CHILD: A First Step Towards Continual Learning

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Theoretical Models of Learning to Learn

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The Existence of A Priori Distinctions Between Learning Algorithms

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Combining Multiple Models with Meta Decision Trees

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Child: A First Step Towards Continual Learning

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Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge

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Dynamical Selection of Learning Algorithms

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Layered concept-learning and dynamically variable bias management

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An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning

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MORE ROBUST CONCEPT LEARNING USING DYNAMICALLY – VARIABLE BIAS

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Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability

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Knowledge Discovery Via Multiple Models

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Knowledge Acquisition form Examples Vis Multiple Models

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Guest Editors' Introduction

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Explicitly biased generalization

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Beyond predictive accuracy : what?

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A Survey of Connectionist Network Reuse Through Transfer

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Meta-cases: Explaining case-based reasoning

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Active bias adjustment for incremental, supervised concept learning

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The Use of Meta-level Information in Learning Situation-Specific Coordination

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Proceedings of the workshop on change of representation and problem reformulation

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On the development of inductive learning algorithms: generating flexible and adaptable concept representations

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Minimal cost complexity pruning of meta-classifiers

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Queries for bias testing

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Data-driven Soft Sensors in the process industry

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Machine learning: a review of classification and combining techniques

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Generalizing to unseen domains: A survey on domain generalization

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A Survey on Federated Learning for Resource-Constrained IoT Devices

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Transfer learning for activity recognition: a survey

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Metalearning: A survey of trends and technologies

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MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud1

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Towards Continual Reinforcement Learning: A Review and Perspectives

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More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

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MetaICL: Learning to Learn In Context

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Microalgae with Artificial Intelligence: A Digitalized Perspective on Genetics, Systems and Products

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What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO

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From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape

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A review on the self and dual interactions between machine learning and optimisation

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Microalgal biorefineries: a systematic review of technological trade-offs and innovation pathways

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A meta-learning approach for genomic survival analysis

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A review of deep learning-based deformable medical image registration

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A multifaceted vision of the human-AI collaboration: a comprehensive review

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Optimizing quantum heuristics with meta-learning

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A survey on neuro-mimetic deep learning via predictive coding

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AEG: Automatic Exploit Generation

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AEMB: An Automated Exploit Mitigation Bypassing Solution

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Evaluation of Two Host-Based Intrusion Prevention Systems

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Automated Crash Analysis and Exploit Generation with Extendable Exploit Model

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Meta-Learning: From Classification to Forecasting, to Optimization, and Beyond

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