Non-statistical weak measurements yield weak values that are outside the range of eigenvalues and are not rare, suggesting that weak values are a property of every preand-post-selected ensemble. They also extend the applicability and valid regime of weak values.

Investigating the “knowing”, a module of the KARDS model proposed by Kumaravadivelu (2012) for teacher education, of Iranian teachers of learner autonomy, this study is an attempt to illuminate some of the yet unexplored areas of teachers’ various types of knowledge of learner autonomy. Furthermore, it attempts to illustrate how Iranian Non-EFL teachers’ knowing affects their practices with reg...

We present an approach and a system that explores the application of interactive machine learning to a branching program-based boosting algorithm—Martingale Boosting. Typically, its performance is based on the ability of a learner to meet a fixed objective and does not account for preferences (e.g., low FPs) arising from an underlying classification problem. We use user preferences gathered on ...

This paper presents an approach with ensemble classifiers using unsupervised data selection for speaker recognition. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. Based on its acoustic characteristics, the speech utterance is divided into several subsets using unsupervised data select...

The exploration-exploitation tradeoff is among the central challenges of reinforcement learning. A hypothetical exact Bayesian learner would provide the optimal solution, but is intractable in general. I show that, however, in the specific case of Gaussian process inference, it is possible to make analytic statements about optimal learning of both rewards and transition dynamics, for nonlinear,...

this paper examines the theoretical rationales and practical aspects of task-based language teaching (tblt) with particular reference to research findings in efl/esl contexts. the definitional scope of the term ‘task’, polarizations in terms of task vs. non-task, and its relation to different language teaching approaches have engendered conceptual and methodological ambiguities.  moreover, fact...

Mutual Boosting is a method aimed at incorporating contextual information to augment object detection. When multiple detectors of objects and parts are trained in parallel using AdaBoost [1], object detectors might use the remaining intermediate detectors to enrich the weak learner set. This method generalizes the efficient features suggested by Viola and Jones [2] thus enabling information inf...

The focus of the paper is the problem of learning kernel operators from empirical data. We cast the kernel design problem as the construction of an accurate kernel from simple (and less accurate) base kernels. We use the boosting paradigm to perform the kernel construction process. To do so, we modify the booster so as to accommodate kernel operators. We also devise an efficient weak-learner fo...

Current re-ranking algorithms for machine translation rely on log-linear models, which have the potential problem of underfitting the training data. We present BoostedMERT, a novel boosting algorithm that uses Minimum Error Rate Training (MERT) as a weak learner and builds a re-ranker far more expressive than log-linear models. BoostedMERT is easy to implement, inherits the efficient optimizati...

We introduce a multi-class generalization of AdaBoost with binary weaklearners. We use a vectorial codification to represent class labels and a multiclass exponential loss function to evaluate classifier responses. This representation produces a set of margin values that provide a range of punishments for failures and rewards for successes. Moreover, the stage-wise optimization of this model in...