vsl samples

for sample training. This means that many people can only use the trained classifier provided by opencv for pedestrian detection. However, the built-in classifier of opencv uses navneetThe samples provided by Dalal and Bill triggs are not necessarily suitable for your application. Therefore, for your specific application scenarios, it is necessary to re-train your classifier. The purpose of this article is here. Re-train the pedestrian detection proc

Introduction to LDA algorithmA. LDA Algorithm Overview:Linear discriminant Analysis (Linear discriminant, LDA), also called Fisher Linear discriminant (Fisher Linear discriminant, FLD), is a classical algorithm for pattern recognition, It was introduced in the field of pattern recognition and artificial intelligence in 1996 by Belhumeur. The basic idea of sexual discriminant analysis is to project the high-dimensional pattern samples to the optimal d

● Log2: log2 value of all feature numbers● None: equal to the number of all features Maximum number of features involved in node splitting● Int: Number● Float: Percentage of all features● Auto: the beginning of all feature numbers● Sqrt: the beginning of all feature numbers● Log2: log2 value of all feature numbers★None: equal to the number of all features Maximum number of features involved in node splitting● Int: Number● Float: Percentage of all features● Auto: the beginning of all featur

)))This becomes the min (J (w))The process of updating W isW:=w?α?▽j (w) w:=w?α?1n? N∑i=1 (HW (xi) yi)? xi)where α is the step length, until J (w) can no longer stopThe biggest problem with the gradient descent method is that it will fall into the local optimum, and each time the cost of the current sample is calculated, it is necessary to traverse all the samples in order to get the costing value, so the calculation speed will be much slower (althoug

The principle of isolation Forest algorithm introduced in this article is described in my blog: Isolation Forest anomaly detection algorithm principle, we only introduce the detailed code implementation process in this article.1, the design and implementation of ItreeFirst, we refer to the construction pseudocode of Itree in the original paper:Write a picture description here1.1 Designing the data structures of the Itree classItree is a binary tree based on the original paper and the pseudo-code

−HW (xi)))This becomes the min (J (w))The process of updating W isW:=w−α∗▽j (W) w:=w−α∗1n∗n∑i=1 (HW (xi) −yi) ∗xi)where α is the step length, until J (W) can no longer stopThe biggest problem with the gradient descent method is that it will fall into the local optimal, and each time we calculate the current sample, cost we need to traverse all the samples to get the cost value, so the calculation speed will be much slower (although the calculation can

http://blog.csdn.net/warmyellow/article/details/5454943Introduction to LDA algorithmA LDA Algorithm Overview:Linear discriminant Analysis (Linear discriminant, LDA), also called Fisher Linear discriminant (Fisher Linear discriminant, FLD), is a classical algorithm for pattern recognition, It was introduced in the field of pattern recognition and artificial intelligence in 1996 by Belhumeur. The basic idea of sexual discriminant analysis is to project the high-dimensional pattern

classification, so that in each round of training focused on the sample will be different, so that the same sample set of different distribution purposes. The updating of the sample weights is based on the weak learner's classification of the samples in the current training set, in particular, to improve the weights of those samples that were incorrectly categorized by the previous round of the weak classi

assumptions and symbol descriptions: Assume that m samples in a space are x1, x2 ,...... XM indicates that each X is a matrix of N rows, which indicates the number of samples belonging to Class I. Suppose there is a class C, then. .................................................................................... Class separation and divergence Matrix ......................................................

Let's start at the beginning. The AUC is a standard used to measure the quality of a classification model. There are a number of such criteria, such as the Eminence Standard in machine learning literature about 10 years ago: Classification accuracy, recall and precision commonly used in the field of information retrieval (IR), and so on. In fact, the measure reflects people's pursuit of "good" classification results, the different measures of the same period reflect people's different understand

number of samples of the tracked targets need to be selected for learning and training. This means that training samples will cover the various deformations and variations in scale, posture and illumination that may occur in the tracked target. In other words, the use of detection method to achieve long-time tracking purposes, training samples for the choice of