Showing Items 371380 of 620 on page 38 of 62: First Previous 33 34 35 36 37 38 39 40 41 42 43 Next Last
About: This program is a C++ implementation of Naive Bayes Classifier, which is a wellknown generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. The program uses the multinomial event model for representation, the maximum likelihood estimate with a Laplace smoothing technique for learning parameters. A sparsedata structure is defined to represent the feature vector in the program to seek higher computational speed. Changes:Initial Announcement on mloss.org.

About: A simple and clear OpenCV based extended Kalman filter(EKF) abstract class implementation,absolutely following standard EKF equations. Special thanks to the open source project of KFilter1.3. It is easy to inherit it to implement a variable state and measurement EKF for computer vision and INS usages. Changes:Initial Announcement on mloss.org.

About: Supervised Latent Semantic Indexing(SLSI) is an supervised feature transformation method. The algorithms in this package are based on the iterative algorithm of Latent Semantic Indexing. Changes:Initial Announcement on mloss.org.

About: This program is used to extract SIFT points from an image. Changes:Initial Announcement on mloss.org.

About: LDPar is an efficient datadriven dependency parser. You can train your own parsing model on treebank data and parse new data using the induced model. Changes:Initial Announcement on mloss.org.

About: This program is used to extract HOG(histograms of oriented gradients) features from images. The integral histogram is used for fast histogram extraction. Both APIs and binary utility are provided. Changes:Initial Announcement on mloss.org.

About: This program is used to find point matches between two images. The procedure can be divided into two parts: 1) use SIFT matching algorithm to find sparse point matches between two images. 2) use "quasidense propagation" algorithm to get "quasidense" point matches. Changes:Initial Announcement on mloss.org.

About: Hofmann, T. 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd ACMSIGIR International Conference on Research and Development in Information Retrieval (Berkeley,Calif.), ACM, New York, 50–57. Changes:Initial Announcement on mloss.org.

About: MeanShift (MS) is a powerful nonparametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. For the purpose of algorithm speedup, an agglomerative MS clustering method called AggloMS was developed, along with its modeseeking ability and convergence property analysis. The method is built upon an iterative query set compression mechanism which is motivated by the quadratic bounding optimization nature of MS. The whole framework can be efficiently implemented in linear running time complexity. Changes:Initial Announcement on mloss.org.

About: The toolbox is to calculate normalized information measures from a given m by (m+1) confusion matrix for objective evaluations of an abstaining classifier. It includes total 24 normalized information measures based on three groups of definitions, that is, mutual information, information divergence, and cross entropy. Changes:Initial Announcement on mloss.org.
