A theoretical framework for learning from a pool of disparate data sources

Ben-David, Shai; Gehrke, Johannes; Schuller, Reba

doi:10.1145/775107.775111

Cited by 5 publications

(8 citation statements)

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“…For example: a) the case of having the same output ("y's") and different inputs ("x's"), which corresponds to the problem of integrating information from heterogeneous databases [7]; or, b) the case of multi-modal learning or learning by components, where the (x, y) data for each of the tasks do not belong to the same space X × Y but data for task t come from a space Xt ×Yt -this is for example the machine vision case of learning to recognize a face by first learning to recognize parts of the face, such as eyes, mouth, and nose [14]. Each of these related tasks can be learned using images of different size (or different representations).…”

Section: Notation and Setupmentioning

confidence: 99%

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Regularized multi--task learning

Evgeniou

Pontil

2004

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

1,196

936

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Past empirical work has shown that learning multiple related tasks from data simultaneously can be advantageous in terms of predictive performance relative to learning these tasks independently. In this paper we present an approach to multi-task learning based on the minimization of regularization functionals similar to existing ones, such as the one for Support Vector Machines (SVMs), that have been successfully used in the past for single-task learning. Our approach allows to model the relation between tasks in terms of a novel kernel function that uses a task-coupling parameter. We implement an instance of the proposed approach similar to SVMs and test it empirically using simulated as well as real data. The experimental results show that the proposed method performs better than existing multi-task learning methods and largely outperforms single-task learning using SVMs.

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Section: Notation and Setupmentioning

confidence: 99%

“…There has been a lot of experimental work showing the benefits of such multi-task learning relative to individual task learning when tasks are related, see [4,11,15,22]. There have also been various attempts to theoretically study multi-task learning, see [4,5,6,7,8,15,23].…”

Section: Introductionmentioning

confidence: 99%

Regularized multi--task learning

Evgeniou

Pontil

2004

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

1,196

936

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“…Examples include: (1) handwritten digit recognition where training examples are provided by several persons, (2) medical diagnosis where predictive (diagnostic) model, say for lung cancer, is estimated using a training data set ofmale and female patients, etc. Incorporating this additional information has lead to approaches known as Multi-Task Learning [1,2,6,10] and, more recently, to Learning with Structured Data (aka SVM+) [9], as discussed next.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, if the group labels of future test samples are given, the problem is Multi-Task Learning (MTL) problem [1,2,6,8]. The goal in multi-task learning is to find t related mapping functions {h, 12'...' It} so that the sum of expected losses for each task…”

Section: Introductionmentioning

confidence: 99%

“…Liang and Cherkassky [5,6] showed empirical validation of SVM+ for classification, and showed its connection to Multi-Task Learning (MTL) classifiers in machine learning [1,2,6,10]. This paper builds upon this recent work and describes a new methodology for regression problems, combining Vapnik's SVM+ approach [9] and the MTL classification scheme [6].…”

Section: Introductionmentioning

confidence: 99%

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SVM+ regression and multi-task learning

Feng

Cherkassky

2009

2009 International Joint Conference on Neural Networks

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Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik [9] proposed general approach to formalizing such problems, known as Learning With Structured Data (LWSD) and its SVM-based optimization formulation called SVM+. Liang and Cherkassky [5,6] showed empirical validation of SVM+ for classification, and its connections to Multi-Task Learning (MTL) approaches in machine learning. This paper builds upon this recent work [5,6,9] and describes a new methodology for regression problems, combining Vapnik's SVM+ regression [9] and the MTL classification setting [6], for regression problems. We also show empirical comparisons between standard SVM regression, SVM+, and proposed SVM+MTL regression method. Practical implementation of new learning technologies, such as SVM+, is often hindered by their complexity, i.e, large number of tuning parameters (vs standard inductive SVM regression). To this end, we provide a practical scheme for model selection that combines analytic selection of parameters for SVM regression [3] and resampling-based methods for selecting model parameters specific to SVM+ and SVM+MTL.

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A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients

Morton

Marzban

Giannoulis

et al. 2014

2014 13th International Conference on Machine Learning and Applications

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Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of inhospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.

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A theoretical framework for learning from a pool of disparate data sources

Cited by 5 publications

References 0 publications

Regularized multi--task learning

Regularized multi--task learning

SVM+ regression and multi-task learning

A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients

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