Kernel methods in machine learning

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.Comment: Published in at http://dx.doi.org/10.1214/009053607000000677 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Developing Learning Methods of Indonesian as a Foreign Language

The present study was conducted which aims at developing teaching methods of Indonesian as a foreign language. This study was carried out for two years in the form of Research and Development design to develop accuracy of teaching methods to be employed to teach the Indonesian language. The study was conducted as an important and crucial issue encountered by prospective teachers of Indonesian as a foreign language to face global challenges in which teachers of Indonesian are urgently required to teach effectively. In addition, this study was conducted to prepare the Indonesian teachers to be professional teachers and ready to face the competitive world of work. In the first year, the research was focused on creating a draft of effective learning methods to teach Indonesian as a foreign language. Consequently, this study was started by analyzing the teaching methods that have been used by various language learning institutions. The second year, the study is mainly focused on trying out and validated the learning methods to ensure their effectiveness to teach Indonesian as a foreign language

Uncertain Photometric Redshifts with Deep Learning Methods

The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multimodal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.Comment: 4 pages, 1 figure, Astroinformatics 2016 conference proceedin

Identifying structural changes with unsupervised machine learning methods

Unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems