Methods of mind-training; concentrated attention and memory,

Title page missing.

Sure methods of improving health, and prolonging life, or, a treatise on the art of living long and comfortably, by regulating the diet and regimen ... : to which is added, the art of training for health, rules for reducing corpulence, and maxims of health ... /

Includes bibliographical references and index.

An outline of the general regulations and methods of teaching in the male national model schools, for the use of the teachers in training.

Cover title.

Modern ideas and methods for school teachers and students in training from a practical teacher's note book,

Mode of access: Internet.

Studies in God's methods of training workers,

Mode of access: Internet.

Toward competent government; the need for training public employees, a review of present methods, and proposals for future development.

Mode of access: Internet.

Discourse analysis for translation and translator training: status, needs, methods

Editorial

The sensitivity of mapping methods to reference data quality:training supervised image classifications with imperfect reference data

The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.