Active Learning with Statistical Models.

Cohn, David; Ghahramani, Zoubin; Jordan, Michael I.

doi:10.21236/ada295617

Cited by 437 publications

(479 citation statements)

References 15 publications

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“…From the perspective of predicting targets, the learning styles can be simplified to transductive learning and inductive learning, where the goal of the former is to predict the D examples, whereas the goal of the latter is to perform prediction on an uncollected dataset. Other well-known learning styles also exist, such as transfer learning (domain transfer) [24], online learning (batch of training dataset) [25], zero-shot learning (extension of label vectors) [26], and active learning (human involvement) [27].…”

Section: The Nature Of Machine Learningmentioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Wang¹,

Zhao²,

Huang³

et al. 2017

Preprint

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The concept of 'big data' has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

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Section: The Nature Of Machine Learningmentioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Wang¹,

Zhao²,

Huang³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Active learning is a paradigm that proposes ways to incrementally learn from unlabeled data, provided the system has available to it an oracle, an entity which knows the correct labeling of all examples [32], [33]. In the case of multimedia information retrieval, this is the user.…”

Section: B Active Learningmentioning

confidence: 99%

“…In the neural network community, several works [32], [35], [39] explored the use of active learning in the context of efficient network training. These techniques fell under a variety of names including query-learning, active learning with membership queries, and selective sampling, though all shared the same underlying approach.…”

Section: Historical Perspectivementioning

confidence: 99%

“…In [32], the point(s) with the minimum expected variance of the learner (with respect to the data) was chosen for active labeling in closed form. The work of Roy and McCallum [33] advanced this idea by noting for many learners, the expected variance could not be found in closed-form, and advocated a computationally feasible model to estimate this value using Monte Carlo techniques.…”

Section: Historical Perspectivementioning

confidence: 99%

“…Whether we are modeling a scenario as a binary classification problem (relevant versus irrelevant images [41] or anomalous versus normal video events [47]) or whether we are looking at multiclass discrimination problems such as the organization and search of news group messages [32], we can rely on classifier-based active learning techniques to help solve our problems. Though many classification algorithms have been explored for active learning, we will focus on an algorithm that has had great applicability to multimedia information retrieval applications in recent years: SVMs.…”

Section: A Classification-based Algorithms: Support Vector Machinesmentioning

confidence: 99%

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Active Learning for Interactive Multimedia Retrieval

et al. 2008

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| As the first decade of the 21st century comes to a close, growth in multimedia delivery infrastructure and public demand for applications built on this backbone are converging like never before. The push towards reaching truly interactive multimedia technologies becomes stronger as our media consumption paradigms continue to change. In this paper, we will profile a technology leading the way in this revolution: active learning. Active learning is a strategy that helps alleviate challenges inherent in multimedia information retrieval through user interaction. We will show how active learning is ideally suited for the multimedia information retrieval problem by giving an overview of the paradigm and component technologies used with special attention given to the application scenarios in which these technologies are useful. Finally, we give insight into the future of this growing field and how it fits into the larger context of multimedia information retrieval.

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Nonlinear experimental design using Bayesian regularized neural networks

Block

2007

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).Novel criteria for designing experiments for nonlinear processes are presented. These criteria improve on a previous methodology in that they can be used to suggest a batch of new experiments to perform (as opposed to a single new experiment) and are also optimized for discovering improved optima of the system response. This is accomplished by using information theoretic criterion, which also heuristically penalize experiments that are likely to result in low (nonoptimal) results. While the methods may be applied to any type of nonlinear-nonparametric model (radial basis functions and generalized linear regression), they are here exclusively considered in conjunction with Bayesian regularized feedforward neural networks. A focus on the application of rapid process development, and how to use repeated experiments to optimize the training procedures of Bayesian regularized neural networks is shown. The presented methods are applied to three case studies. The first two case studies involve simulations of one and two-dimensional (2-D) nonlinear regression problems. The third case study involves real historical data from bench-scale fermentations generated in our laboratory. It is shown that using the presented criteria to design new experiments can greatly increase a feedforward neural network's ability to predict global optima.

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Active Learning with Statistical Models.

Cited by 437 publications

References 15 publications

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Active Learning for Interactive Multimedia Retrieval

Nonlinear experimental design using Bayesian regularized neural networks

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