However, for many applications, this assumption is unrealistic.

Sampling errors, human errors, modeling errors and instrument errors may preclude the possibility of knowing the data matrix X exactly, where X=[xis not accurate.

A hidden underlying assumption is that errors are confined to the output y.

That is, the input data are not corrupted with noise; or even when noise is present in the data, its effect is ignored in the learning formulation.

emitter, China faces the challenge of reducing the carbon intensity of its economy while also fostering economic growth in provinces where development is lagging.

Although China is often seen as a homogeneous entity, it is a vast country with substantial regional variation in physical geography, economic development, infrastructure, population density, demographics, and lifestyles (1).

Articles will remain available for view or download, where access rights already apply.

Less apparent is that this relationship between developed and developing can exist within a single country’s borders, with rich regions consuming and exporting high-value goods and services that depend upon production of low-cost and emission-intensive goods and services from poorer regions in the same country.

A computer-implemented method for determining a boundary for binary classification includes providing a data set, initializing a value for noise in the data set, and determining a hyperplane dividing the data set and a slack variable given a current value for noise. A computer-implemented method for determining a boundary for binary classification includes providing a data set, initializing a value for noise in the data set, and determining a hyperplane dividing the data set and a slack variable given a current value for noise.

The method further includes updating the value for noise and the slack variable given the hyperplane, and determining the hyperplane to be the boundary for binary classification of the data set upon determining a termination criterion to be met, wherein elements of the data set are classified according to the boundary.determining a hyperplane dividing the data set and a slack variable for each vector in the data set given a current value for uncertainty, wherein a weight of each vector on the hyperplane is varied according to the current value for uncertainty for the respective vector and wherein the weight of a respective vector is reduced with increased uncertainty;determining the hyperplane to be the boundary for binary classification of the data set upon determining a termination criterion to be met, wherein the vectors of the data set are classified according to the boundary., wherein determining the hyperplane dividing the data set and the slack variable given the current value for noise and updating the value for noise and the slack variable given the hyperplane are performed iteratively until the termination criteria is met.8.

همچنین اشتغال ایجادی برای افزایش یک درصدی در ارزش افزوده صنعت گردشگری، بیانگر سهم 15 درصدی اشتغال در خود بخش گردشگری و 54 درصدیدر سایر فعالیت ها می باشد.

features, search, payment options and informational pages on Taylor & Francis Online will be unavailable during this scheduled release.

For example, consider the problem of classifying sentences from speech recognition output for call-routing applications.

## You must have an account to comment. Please register or login here!