Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.

In many applications, such as credit default prediction and medical image recognition, test inputs are available in addition to the labeled training examples. We propose a method to incorporate the test inputs into learning.

Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.

Program execution speed on modem computers is sensitive, by a factor of two or more, to the order in which instructions are presented to the processor. Torealize potential execution efficiency, an optimizing compiler must employ a heuristic algorithm for instruction scheduling. Such algorithms are painstakingly handcrafted, which is expensive and time-consuming. We show how to cast the instruction scheduling problem as a learning task, obtaining theheuristic scheduling algorithm automatically. Our focus is the narrower problem of scheduling straight-line code (also called basic blocks of instructions). Our empirical results show that just a few features are adequate forquite good performance at this task for a real modem processor, and that any of several supervised learning methods perform nearly optimally withrespect to the features used.

In many applications, such as credit default prediction and medical image recognition,test inputs are available in addition to the labeled training examples. We propose a method to incorporate the test inputs into learning.

Most previous work in inductive learning has concentrated on learning to classify. However, there are many applications in which it is desirable to order rather than classify instances. An example might be a personalized email filter that gives a priority ordering to unread mail. Here we will consider the problem of learning how to construct such orderings, given feedback in the form of preference judgments, i.e., statements that one instance should be ranked ahead of another. Such orderings could be constructed based on a learned classifier or regression model, and in fact often are.

This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications.

Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.

In recent years, there has been a flurry of research into empirical, corpus-based learning approaches to natural language processing (NLP). Most empirical NLP work to date has focused on relatively low-level language processing such as part-of-speech tagging, text segmentation, and syntactic parsing. The success of these approaches has stimulated research in using empirical learning techniques in other facets of NLP, including semantic analysis -- uncovering the meaning of an utterance. This article is an introduction to some of the emerging research in the application of corpus-based learning techniques to problems in semantic interpretation. In particular, we focus on two important problems in semantic interpretation, namely, word-sense disambiguation and semantic parsing.

Instance-based learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications the new distance metrics achieve higher classification accuracy on average than three previous distance functions on those datasets that have both nominal and continuous attributes.