Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

/pdf/scikit-learn-machine-learning-in-python-23clfh9q83.pdf

Scikit-learn: Machine Learning in Python

LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

/pdf/libsvm-a-library-for-support-vector-machines-2gdxasg9zv.pdf

LIBSVM: A library for support vector machines

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

J. Appl. Cryst.の発刊に際して

Comparative Study of Electrical Properties of Chalcogenide Films Produced by Reaction of Cu, Ag, Ni and Nicu with Sb2s3 in Hot Wall Epitaxy

Co2TiSi films were grown by molecular beam epitaxy on GaAs(001) and analyzed using reflection high-energy electron diffraction, and electron microscopy. In addition, X-ray diffraction was combined with lattice parameter calculations by density functional theory comparing the L21 and B2 structures and considering the influence of non-stoichiometry. Columnar growth is found and attributed to inhomogeneous epitaxial strain from non-random alloying. In films with thicknesses up to 13 nm, these columns may be the origin of perpendicular magnetization with the easy axis perpendicular to the sample surface. We found L21 and B2 ordered regions, however the [Co]/[Ti]-ratio is changing in dependence of the position in the film. The resulting columnar structure is leading to anisotropic B2-ordering with the best order parallel to the axes of the columns.

/pdf/structural-properties-of-co2tisi-films-on-gaas-001-1fn4ket0ph.pdf

Michael Hanke

Papers

Comparative Study of Electrical Properties of Chalcogenide Films Produced by Reaction of Cu, Ag, Ni and Nicu with Sb2s3 in Hot Wall Epitaxy

Structural properties of Co2TiSi films on GaAs(001)