1. New Theoretical Directions In Machine Learning

   • David Haussler

   Editorial Commentary Pages: 281 - 284

2. New theoretical directions in machine learning

   • David Haussler

   Guest Editorial Pages: 281 - 284

3. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm

   • Nick Littlestone

   OriginalPaper Pages: 285 - 318

4. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

   • Nick Littlestone

   OriginalPaper Pages: 285 - 318

5. Queries and Concept Learning

   • Dana Angluin

   OriginalPaper Pages: 319 - 342

6. Queries and concept learning

   • Dana Angluin

   OriginalPaper Pages: 319 - 342

7. Learning From Noisy Examples

   • Dana Angluin
   • Philip Laird

   OriginalPaper Pages: 343 - 370

8. Learning from noisy examples

   • Dana Angluin
   • Philip Laird

   OriginalPaper Pages: 343 - 370

9. Criteria for Polynomial-Time (Conceptual) Clustering

   • Leonard Pitt
   • Robert E. Reinke

   OriginalPaper Pages: 371 - 396

10. Criteria for polynomial-time (conceptual) clustering

    • Leonard Pitt
    • Robert E. Reinke

    OriginalPaper Pages: 371 - 396

11. News and notes

    News Pages: 397 - 398

12. News and Notes

    News Pages: 397 - 398

13. Table of Contents: Volume 1 and 2

    Table of Contents Pages: 399 - 400

14. Announcing a new journal

    Announcement Pages: i - ii