Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity

BACKGROUND The computation of pattern motion in visual area MT based on motion input from area V1 has been investigated in many experiments and models attempting to replicate the main mechanisms. Two different core conceptual approaches were developed to explain the findings. In integrationist models the key mechanism to achieve pattern selectivity is the nonlinear integration of V1 motion acti...

متن کامل

Combining Multiple Neural Nets for Visual Feature Selection and Classification

and lassi ation Gunther Heidemann and Helge Ritter AG Neuroinformatik, University of Bielefeld 33501 Bielefeld, Germany email: gheidema te hfak.uni-bielefeld.de De ember 15, 1999 Abstra t We present a system for obje t re ognition in real images employing three di erent types of neural networks, whi h a omplish feature extra tion and lassi ation. The main advantages of the method are its portab...

متن کامل

Combining feature selection and feature reduction for protein classification

We use the n-grams descriptors for a protein classification task. As they are automatically generated, we obtain many irrelevant and/or redundant descriptors. In this paper, we evaluate various strategies of feature selection and feature reduction. First, we evaluate separately the efficiency of a filtering feature selection algorithm and a feature reduction on the basis of a singular value dec...

متن کامل

Combining Feature Subsets in Feature Selection

In feature selection, a part of the features is chosen as a new feature subset, while the rest of the features is ignored. The neglected features still, however, may contain useful information for discriminating the data classes. To make use of this information, the combined classifier approach can be used. In our paper we study the efficiency of combining applied on top of feature selection/ex...

متن کامل

Combining multiple classifiers for wrapper feature selection

Wrapper feature selection methods are widely used to select relevant features. However, wrappers only use a single classifier. The downside to this approach is that each classifier will have its own biases and will therefore select very different features. In order to overcome the biases of individual classifiers, this study introduces a new data mining method called wrapper-based decision tree...

متن کامل

ذخیره 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: PLoS ONE

سال: 2011

ISSN: 1932-6203

DOI: 10.1371/journal.pone.0021254