Effects of Numerical Errors on Sample Mahalanobis Distances
نویسندگان
چکیده
منابع مشابه
Learning Local Invariant Mahalanobis Distances
For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, and tolerance for image transformations was primarily achieved by using robust...
متن کاملFuzzy C-Means Algorithm Based on Standard Mahalanobis Distances
Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...
متن کاملUnsupervised Clustering Algorithm Based on Normalized Mahalanobis Distances
Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...
متن کاملthe effects of error correction methods on pronunciation accuracy
هدف از انجام این تحقیق مشخص کردن موثرترین متد اصلاح خطا بر روی دقت آهنگ و تاکید تلفظ کلمه در زبان انگلیسی بود. این تحقیق با پیاده کردن چهار متد ارائه اصلاح خطا در چهار گروه، سه گروه آزمایشی و یک گروه تحت کنترل، انجام شد که گروه های فوق الذکر شامل دانشجویان سطح بالای متوسط کتاب اول passages بودند. گروه اول شامل 15، دوم 14، سوم 15 و آخرین 16 دانشجو بودند. دوره مربوطه به مدت 10 هفته ادامه یافت و د...
15 صفحه اولSample Complexity of Learning Mahalanobis Distance Metrics
Metric learning seeks a transformation of the feature space that enhances prediction quality for a given task. In this work we provide PAC-style sample complexity rates for supervised metric learning. We give matching lowerand upper-bounds showing that sample complexity scales with the representation dimension when no assumptions are made about the underlying data distribution. In addition, by ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2016
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2015edp7348