Kernel-based classification using quantum mechanics
نویسندگان
چکیده
منابع مشابه
Kernel-based classification using quantum mechanics
This paper introduces a new nonparametric estimation approach inspired from quantum mechanics. Kernel density estimation associates a function to each data sample. In classical kernel estimation theory the probability density function is calculated by summing up all the kernels. The proposed approach assumes that each data sample is associated with a quantum physics particle that has a radial a...
متن کاملPhase space formalisms of quantum mechanics with singular kernel
The equivalence of the Rivier-Margenau-Hill and Born-Jordan-Shankara phase space formalisms to the conventional operator approach of quantum mechanics is demonstrated. It is shown that in spite of the presence of singular kernels the mappings relating phase space functions and operators back and forth are possible. Ref: Physics Letters A 231 (1997) 304, electronic version with permission of Els...
متن کاملQuestion Classification Using HDAG Kernel
This paper proposes a machine learning based question classification method using a kernel function, Hierarchical Directed Acyclic Graph (HDAG) Kernel. The HDAG Kernel directly accepts structured natural language data, such as several levels of chunks and their relations, and computes the value of the kernel function at a practical cost and time while reflecting all of these structures. We exam...
متن کاملKernel sparse representation based classification
Sparse representation has attracted great attention in the past few years. Sparse representation based classification (SRC) algorithm was developed and successfully used for classification. In this paper, a kernel sparse representation based classification (KSRC) algorithm is proposed. Samples are mapped into a high dimensional feature space first and then SRC is performed in this new feature s...
متن کاملBCI Signal Classification using a Riemannian-based kernel
The use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms signif...
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: Pattern Recognition
سال: 2007
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2006.08.011