Multi-Instance Kernels

Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, research started investigating kernels for structured data. This paper considers kernels for multi-instance problems a class of concepts on individuals represented by sets. The main result of this paper is a kernel on multi-instance data that can be shown to separate positive and negative sets under natural assumptions. This kernel compares favorably with state of the art multi-instance learning algorithms in an empirical study. Finally, we give some concluding remarks and propose future work that might further improve the results.

[1]  Jan Ramon,et al.  Multi instance neural networks , 2000, ICML 2000.

[2]  Yann Chevaleyre,et al.  A Framework for Learning Rules from Multiple Instance Data , 2001, ECML.

[3]  Hendrik Blockeel,et al.  Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..

[4]  Qi Zhang,et al.  EM-DD: An Improved Multiple-Instance Learning Technique , 2001, NIPS.

[5]  Giancarlo Ruffo,et al.  Learning single and multiple instance decision tree for computer security applications , 2000 .

[6]  Ralf Herbrich,et al.  Learning Kernel Classifiers: Theory and Algorithms , 2001 .

[7]  Stefan Wrobel,et al.  Transformation-Based Learning Using Multirelational Aggregation , 2001, ILP.

[8]  Jun Wang,et al.  Solving the Multiple-Instance Problem: A Lazy Learning Approach , 2000, ICML.

[9]  James D. Keeler,et al.  Integrated Segmentation and Recognition of Hand-Printed Numerals , 1990, NIPS.

[10]  Peter Auer,et al.  On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach , 1997, ICML.

[11]  Vladimir Cherkassky,et al.  Learning from Data: Concepts, Theory, and Methods , 1998 .

[12]  Sally A. Goldman,et al.  Multiple-Instance Learning of Real-Valued Data , 2001, J. Mach. Learn. Res..

[13]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[14]  David Haussler,et al.  Convolution kernels on discrete structures , 1999 .

[15]  David Page,et al.  Multiple Instance Regression , 2001, ICML.

[16]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[17]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[18]  Ashwin Srinivasan,et al.  Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction , 1996, Artif. Intell..

[19]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[20]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.