1. Introduction

   • Ryszard S. Michalski

   Introduction Pages: 109 - 110

2. Introduction

   • Ryszard S. Michalski

   EditorialNotes Pages: 109 - 110

3. Inferential theory of learning as a conceptual basis for multistrategy learning

   • Ryszard S. Michalski

   OriginalPaper Pages: 111 - 151

4. Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning

   • Ryszard S. Michalski

   OriginalPaper Pages: 111 - 151

5. Multistrategy learning and theory revision

   • Lorenza Saitta
   • Marco Botta
   • Filippo Neri

   OriginalPaper Pages: 153 - 172

6. Multistrategy Learning and Theory Revision

   • Lorenza Saitta
   • Marco Botta
   • Filippo Neri

   OriginalPaper Pages: 153 - 172

7. Learning causal patterns: Making a transition from data-driven to theory-driven learning

   • Michael Pazzani

   OriginalPaper Pages: 173 - 194

8. Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning

   • Michael Pazzani

   OriginalPaper Pages: 173 - 194

9. Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou–Fasman Algorithm for Protein Folding

   • Richard Maclin
   • Jude W. Shavlik

   OriginalPaper Pages: 195 - 215

10. Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding

    • Richard Maclin
    • Jude W. Shavlik

    OriginalPaper Pages: 195 - 215

11. Balanced Cooperative Modeling

    • Katharina Morik

    OriginalPaper Pages: 217 - 235

12. Balanced cooperative modeling

    • Katharina Morik

    OriginalPaper Pages: 217 - 235

13. Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies

    • Gheorghe Tecuci

    OriginalPaper Pages: 237 - 261

14. Plausible justification trees: A framework for deep and dynamic integration of learning strategies

    • Gheorghe Tecuci

    OriginalPaper Pages: 237 - 261

15. Publisher's note

    Announcement Pages: i - i