Struck: Structured Output Tracking with Kernels
نویسندگان
چکیده
منابع مشابه
Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels
In this paper, we introduce a novel approach, called Input Output Kernel Regression (IOKR), for learning mappings between structured inputs and structured outputs. The approach belongs to the family of Output Kernel Regression methods devoted to regression in feature space endowed with some output kernel. In order to take into account structure in input data and benefit from kernels in the inpu...
متن کاملReaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function
Abstract: Enzyme function prediction problem is usually solved using annotation transfer methods. These methods are suitable in cases where the function of the new protein is previously characterized and included in the taxonomy such as EC hierarchy. However, given a new function that is not previously described, these approaches arguably do not offer adequate support for the human expert. In t...
متن کاملRule Ensemble Learning Using Hierarchical Kernels in Structured Output Spaces
The goal in Rule Ensemble Learning (REL) is simultaneous discovery of a small set of simple rules and their optimal weights that lead to good generalization. Rules are assumed to be conjunctions of basic propositions concerning the values taken by the input features. It has been shown that rule ensembles for classification can be learnt optimally and efficiently using hierarchical kernel learni...
متن کاملMulti-label Classification with Output Kernels
Although multi-label classification has become an increasingly important problem in machine learning, current approaches remain restricted to learning in the original label space (or in a simple linear projection of the original label space). Instead, we propose to use kernels on output label vectors to significantly expand the forms of label dependence that can be captured. The main challenge ...
متن کاملLearning Output Kernels with Block Coordinate Descent
We propose a method to learn simultaneously a vector-valued function and a kernel between its components. The obtained kernel can be used both to improve learning performance and to reveal structures in the output space which may be important in their own right. Our method is based on the solution of a suitable regularization problem over a reproducing kernel Hilbert space of vector-valued func...
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: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2016
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2015.2509974