35,716 research outputs found
A review of multi-instance learning assumptions
Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this âstandard MI assumptionâ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area
Efficient Output Kernel Learning for Multiple Tasks
The paradigm of multi-task learning is that one can achieve better
generalization by learning tasks jointly and thus exploiting the similarity
between the tasks rather than learning them independently of each other. While
previously the relationship between tasks had to be user-defined in the form of
an output kernel, recent approaches jointly learn the tasks and the output
kernel. As the output kernel is a positive semidefinite matrix, the resulting
optimization problems are not scalable in the number of tasks as an
eigendecomposition is required in each step. \mbox{Using} the theory of
positive semidefinite kernels we show in this paper that for a certain class of
regularizers on the output kernel, the constraint of being positive
semidefinite can be dropped as it is automatically satisfied for the relaxed
problem. This leads to an unconstrained dual problem which can be solved
efficiently. Experiments on several multi-task and multi-class data sets
illustrate the efficacy of our approach in terms of computational efficiency as
well as generalization performance
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
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