Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
The weighted majority algorithm
Information and Computation
Efficient distribution-free learning of probabilistic concepts
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Image-based navigation through large-scale environments
Image-based navigation through large-scale environments
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Fat-shattering and the learnability of real-valued functions
Journal of Computer and System Sciences
Learning of depth two neural networks with constant fan-in at the hidden nodes (extended abstract)
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
PAC learning of one-dimensional patterns
Machine Learning
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
On-line evaluation and prediction using linear functions
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A Note on Learning from Multiple-Instance Examples
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
On Restricted-Focus-of-Attention Learnability of Boolean Functions
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
Efficient learning with virtual threshold gates
Information and Computation
Learning with unreliable boundary queries
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Learning with restricted focus of attention
Journal of Computer and System Sciences
Relative loss bounds for multidimensional regression problems
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Approximating hyper-rectangles: learning and pseudorandom sets
Journal of Computer and System Sciences - Fourteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
On learning in the presence of unspecified attribute values
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Structural results about exact learning with unspecified attribute values
Journal of Computer and System Sciences - Eleventh annual conference on computational learning theory&slash;Twelfth Annual IEEE conference on computational complexity
Agnostic learning of geometric patterns
Journal of Computer and System Sciences
Machine Learning
Machine Learning
Multiple-Instance Learning of Real-Valued Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Real-Valued Multiple-Instance Learning with Queries
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from examples with unspecified attribute values
Information and Computation
Learning from ambiguity
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
Worst-case quadratic loss bounds for prediction using linear functions and gradient descent
IEEE Transactions on Neural Networks
Real-Valued Multiple-Instance Learning with Queries
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Efficiently Approximating Weighted Sums with Exponentially Many Terms
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Multiple instance learning of real valued data
The Journal of Machine Learning Research
On approximating weighted sums with exponentially many terms
Journal of Computer and System Sciences
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
Real-valued multiple-instance learning with queries
Journal of Computer and System Sciences
Multiple Instance Learning with Multiple Objective Genetic Programming for Web Mining
Applied Soft Computing
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Unsupervised multiple-instance learning for functional profiling of genomic data
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Recently there has been significant research in multiple-instance learning, yet most of this work has only considered this model when there are Boolean labels. However, in many of the application areas for which the multiple-instance model fits, real-valued labels are more appropriate than Boolean labels. We define and study a real-valued multiple-instance model in which each multiple-instance example (bag) is given a real-valued label in [0, 1] that indicates the degree to which the bag satisfies the target concept. To provide additional structure to the learning problem, we associate a real-valued label with each point in the bag. These values are then combined using a real-valued aggregation operator to obtain the label for the bag. We then present on-line agnostic algorithms for learning real-valued multiple-instance geometric concepts defined by axis-aligned boxes in constant-dimensional space and describe several possible applications of these algorithms. We obtain our learning algorithms by reducing the problem to one in which the exponentiated gradient or gradient descent algorithm can be used. We also give a novel application of the virtual weights technique. In typical applications of the virtual weights technique, all of the concepts in a group have the same weight and prediction, allowing a single “representative” concept from each group to be tracked. However, in our application there are an exponential number of different weights and possible predictions. Hence, boxes in each group have different weights and predictions, making the computation of the contribution of a group significantly more involved. However, we are able to both keep the number of groups polynomial in the number of trials and efficiently compute the overall prediction.