TV-tree的c實現源碼，對應原文章K.-I. Lin, H. V. Jagadish, C. Faloutsos: The TV-Tree: An Index Structure for High-DIMENSIONAL Data.
上傳時間： 2014-11-26
上傳用戶：lxm
X-tree的C++源碼，對應文章S. Berchtold, D. A. Keim, H.-P. Kriegel: The X-tree : An Index Structure for High-DIMENSIONAL Data.
上傳時間： 2015-08-22
上傳用戶：1101055045
Feature selection is a preprocessing technique frequently used in data mining and machine learning tasks. It can reduce DIMENSIONALity, remove irrelevant data, increase learning accuracy, and improve results comprehensibility. FCBF is a fast correlation-based filter algorithm designed for high-DIMENSIONAL data and has been shown effective in removing both irrelevant features and redundant features
標簽： preprocessing frequently selection technique
上傳時間： 2014-01-19
上傳用戶：lindor
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-DIMENSIONAL embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標簽： approach combines particle tracking
上傳時間： 2016-01-02
上傳用戶：yy541071797
In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high DIMENSIONAL state spaces, as we demonstrate in experiments.
標簽： relational filtering consider problem
上傳時間： 2016-01-02
上傳用戶：海陸空653
We present a particle filter construction for a system that exhibits time-scale separation. The separation of time-scales allows two simplifications that we exploit: i) The use of the averaging principle for the DIMENSIONAL reduction of the system needed to solve for each particle and ii) the factorization of the transition probability which allows the Rao-Blackwellization of the filtering step. Both simplifications can be implemented using the coarse projective integration framework. The resulting particle filter is faster and has smaller variance than the particle filter based on the original system. The convergence of the new particle filter to the analytical filter for the original system is proved and some numerical results are provided.
標簽： construction separation time-scale particle
上傳時間： 2016-01-02
上傳用戶：fhzm5658
Using Gaussian elimination to solve linear equations. // In this version, we allow matrix of any size. This is done by treating // the name of a 2-DIMENSIONAL array as pointer to the beginning of the // array. This makes use of the fact that arrays in C are stored in // row-major order.
標簽： elimination equations Gaussian version
上傳時間： 2016-02-14
上傳用戶：hxy200501
A Web Tutorial on Discrete Features of Bayes Decision Theory This applet allows for the calculation of the decision boundary given a three DIMENSIONAL feature vector. Specifically, by stipulating the variables such as the priors, and the conditional likelihoods of each feature with respect to each class, the changing decision boundary will be displayed.
標簽： calculation Tutorial Discrete Decision
上傳時間： 2013-12-22
上傳用戶：hxy200501
GloptiPoly 3: moments, optimization and semidefinite programming. Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of Moments (GPM), an infinite-DIMENSIONAL optimization problem which can be viewed as an extension of the classical problem of moments [8]. From a theoretical viewpoint, the GPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to cite a few. In addition, and despite a rather simple and short formulation, the GPM has a large number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For an account of various methodologies as well as some of potential applications, the interested reader is referred to [1, 2] and the nice collection of papers [5].
標簽： optimization semidefinite programming GloptiPoly
上傳時間： 2016-06-05
上傳用戶：lgnf
JLAB is a set of Matlab functions I have written or co-written over the past fifteen years for the purpose of analyzing data. It consists of four hundred m-files spanning thirty thousand lines of code. JLAB includes functions ranging in complexity from one-line aliases to high-level algorithms for certain specialized tasks. These have been collected together and made publicly available for you to use, modify, and --- subject to certain very reasonable constraints --- to redistribute. Some of the highlights are: a suite of functions for the rapid manipulation of multi-component, potentially multi-DIMENSIONAL datasets a systematic way of dealing with datasets having components of non-uniform length tools for fine-tuning figures using compact, straightforward statements and specialized functions for spectral and time / frequency analysis, including advanced wavelet algorithms developed by myself and collaborators.
標簽： co-written functions the fifteen
上傳時間： 2014-01-26
上傳用戶：hjshhyy
