Appears in Proceedings of the 1992 Machine Learning Workshop on Integrated Learning in Real Domains , Aberdeen , Scotland , July , 1992 Combining

The Ninth International Conference on Machine Learning

■ The Ninth International Conference on Machine Learning was held in Aberdeen, Scotland, from 1–3 July 1992, with 198 participants in attendance. The conference covered a broad range of topics drawn from the general area of machine learning, including concept-learning algorithms, clustering, speedup learning, formal analysis of learning systems, neural networks, genetic algorithms, and applicat...

The Effect of a Learning and Study Skills Workshop on Talented Students’ Learning and Study Strategies in Isfahan University of Medical Sciences

Introduction: The significance of learning strategies used by students in their academic achievement has been argued. In this study, the effect of a study skills educational workshop on employing learning and study strategies by talented students of Isfahan University of Medical Sciences has been investigated. Methods: This quasi-experimental study was performed on 40 talented students of Isfa...

Image alignment via kernelized feature learning

Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

In Proceedings of the ICML Workshop on Structural Knowledge Transfer for Machine Learning . Pittsburgh , PA , July 2006 . Transfer Learning with Markov