Hybrid Generative Models for Two-Dimensional Datasets

infinite dimensional garch models

مدلهای گارچ در فضاهای هیلبرت پایان نامه حاضر شامل دو بخش می باشد. در قسمت اول مدلهای اتورگرسیو تعمیم یافته مشروط به ناهمگنی واریانس در فضاهای هیلبرت را معرفی، مفاهیم ریاضی مورد نیاز در تحلیل این مدلها در دامنه زمان را مطرح کرده و آنها را مورد بررسی قرار می دهیم. بر اساس پیشرفتهایی که اخیرا در زمینه تئوری داده های تابعی و آماره های عملگری ایجاد شده است، فرآیندهایی که دارای مقادیر در فضاهای ...

Classification with Hybrid Generative/Discriminative Models

Although discriminatively-trained classifiers are usually more accurate when labeled training data is abundant, previous work has shown that when training data is limited, generative classifiers can out-perform them. This paper describes a hybrid model in which a high-dimensional subset of the parameters are trained to maximize generative likelihood, and another, small, subset of parameters are...

Generative Oversampling for Mining Imbalanced Datasets

One way to handle data mining problems where class prior probabilities and/or misclassification costs between classes are highly unequal is to resample the data until a new, desired class distribution in the training data is achieved. Many resampling techniques have been proposed in the past, and the relationship between resampling and cost-sensitive learning has been well studied. Surprisingly...

Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning

Most methods in machine learning are described as either discriminative or generative. The former often attain higher predictive accuracy, while the latter are more strongly regularized and can deal with missing data. Here, we propose a new framework to combine a broad class of discriminative and generative models, interpolating between the two extremes with a multiconditional likelihood object...

Hybrid Generative-Discriminative Models for Speech and Speaker Recognition