In most classical frameworks for learning from examples, it is assumed that examples are randomly drawn and presented to the learner. In this paper, we consider the possibility of a more active learner who is allowed to choose his/her own examples. Our investigations are carried out in a function approximation setting. In particular, using arguments from optimal recovery (Micchelli and Rivlin, 1976), we develop an adaptive sampling strategy (equivalent to adaptive approximation) for arbitrary approximation schemes. We provide a general formulation of the problem and show how it can be regarded as sequential optimal recovery. We demonstrate the application of this general formulation to two special cases of functions on the real line 1) monotonically increasing functions and 2) functions with bounded derivative. An extensive investigation of the sample complexity of approximating these functions is conducted yielding both theoretical and empirical results on test functions. Our theoretical results (stated insPAC-style), along with the simulations demonstrate the superiority of our active scheme over both passive learning as well as classical optimal recovery. The analysis of active function approximation is conducted in a worst-case setting, in contrast with other Bayesian paradigms obtained from optimal design (Mackay, 1992).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 272-279).

Educational response to climate change is one of the measures to prepare people to combat climate change. This thesis explores the lived experiences of secondary Science teachers from Kathmandu Valley on the perception of climate change, the way they handled climate change issues in the classroom setting, the problems and challenges they came across in climate change communication in the classrooms and the relevance of existing secondary Science curriculum in relation to climate change. The thesis is built upon the study of secondary Science curriculum, relevant literature on climate change education and the interviews with secondary Science teachers, teaching Science at secondary level in different schools of Kathmandu Valley.  The results showed that the teachers were convinced and concerned on the ongoing climate change and stressed on knowledge for climate change actions; they were found to introduce climate change issues contextually and relating to the topics like greenhouse effect, ozone layer depletion they teach; lack of resources, exclusion of climate change in the secondary Science curriculum, their own limited knowledge on climate change, the unpractical theory and marks oriented educational system, and shifting of the responsibilities by the students hindered effective climate change communication in the classroom settings. The findings have been discussed in relation to social learning theory and relevant literature.

Machine learning has enjoyed astounding practical success in a wide range of applications in recent years-practical success that often hurries ahead of our theoretical understanding. The standard framework for machine learning theory assumes full supervision, that is, training data consists of correctly labeled iid examples from the same task that the learned classifier is supposed to be applied to. However, many practical applications successfully make use of the sheer abundance of data that is currently produced. Such data may not be labeled or may be collected from various sources. The focus of this thesis is to provide theoretical analysis of machine learning regimes where the learner is given such (possibly large amounts) of non-perfect training data. In particular, we investigate the benefits and limitations of learning with unlabeled data in semi-supervised learning and active learning as well as benefits and limitations of learning from data that has been generated by a task that is different from the target task (domain adaptation learning). For all three settings, we propose Probabilistic Lipschitzness to model the relatedness between the labels and the underlying domain space, and we discuss our suggested notion by comparing it to other common data assumptions.

No description available.

Software systems will eventually contribute to their own maintenance using implementations of self-awareness. Understanding how to specify, model, and implement software with a sense of self is a daunting problem. This research draws inspiration from the automatic functioning of a gimbal---a self-righting mechanical device that supports an object and maintains the orientation of this object with respect to gravity independently of its immediate operating environment. A software gimbal exhibits a self-righting feature that provisions software with two auxiliary mechanisms: a historical mechanism and a reflective mechanism. The historical mechanism consists of behavior classifiers trained on statistical models of data that are collected from executions of the program that exhibit known behaviors of the program. The reflective mechanism uses the historical mechanism to assess an ongoing or selected execution. This dissertation presents techniques for the identification and modeling of program execution features as statistical models. It further demonstrates how statistical machine-learning techniques can be used to manipulate these models and to construct behavior classifiers that can automatically detect and label known program behaviors and detect new unknown behaviors. The thesis is that statistical summaries of data collected from a software program's executions can model and predict external behaviors of the program. This dissertation presents three control-flow features and one value-flow feature of program executions that can be modeled as stochastic processes exhibiting the Markov property. A technique for building automated behavior classifiers from these models is detailed. Empirical studies demonstrating the efficacy of this approach are presented. The use of these techniques in example software engineering applications in the categories of software testing and failure detection are described.

NONE

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Computer Science." Includes bibliographical references (p. 238-261).

As the title reveals this is a study of female college students alcohol habits in Kalmar,Sweden. During the spring of 2012 a total of 118 female students at Linnaeus universityanswered a survey about their alcohol habits. The survey reveled that as many as 67%percent of the answering female students (according to Audit) have risky drinking habits.The drinking habits are explained using Albert Banduras social learning theory in contextto the Scandinavian drinking pattern. The study concludes that female alcohol habits needeven further research. Furthermore the study shows that student initiation have an impacton the female drinking habits and that expectations of that students drink are to some extentimportant to the development of hazardous drinking habits.

Imaging has revealed that brain activation of verbs with verifiable products (‘throw, kick’) activate language areas as well as the motor cortex responsible for the performance of the action described. An exploratory comparison of eye related verbs with no verifiable products (‘observe’) to mouth related verbs with verifiable products (‘shout’) has revealed a similar activation pattern. Thus in order to further study mental action verbs with no verifiable products, the present two-part study used words that were suitable across two modalities (e.g. you can ‘perceive’ both through vision and audition) and compare them to themselves under differing contexts of auditory and visual verbs so as to eliminate any word characteristics differences, as well as explored the two modalities directly. The primary purpose was to delineate whether associative learning or the mirror systems theory might better account for the acquisition of this unique subclass of verbs. Results suggest that Mirror systems theory more likely accounts for the observed cognitive processing differences between the two verbs. Keywords: Verbs, language, Event-related potentials, abstract, associative learning theory, mirror systems theory.