TheSparksFoundation_Task-4_Decision-Trees 决策树是用于分析数据的图形表示。 决策树以这种方式为我们提供数据，我们可以轻松地读取，理解和分析数据。 决策树算法属于监督学习算法家族。 ...使用决策树的目的是创建一个训练模型，该模型可以通过学习从先前数据（训练数据）推断出的简单决策规则来预测目标变量的类或值

Behavior Designer最新版1.6.6 - Behavior Trees for Everyone

基于行为树的编程方法，可以和强化学习无缝结合，比状态机更好用，值得学习

String algorithms are a traditional area of study in computer science. In recent years their importance has grown dramatically with the huge increase of electronically stored text and of molecular sequence data (DNA or protein sequences) produced by various genome projects. This 1997 book is a general text on computer algorithms for string processing. In addition to pure computer science, the book contains extensive discussions on biological problems that are cast as string problems, and on methods developed to solve them. It emphasises the fundamental ideas and techniques central to today's applications. New approaches to this complex material simplify methods that up to now have been for the specialist alone. With over 400 exercises to reinforce the material and develop additional topics, the book is suitable as a text for graduate or advanced undergraduate students in computer science, computational biology, or bio-informatics. Its discussion of current algorithms and techniques also makes it a reference for professionals.

非影印版，可以不用OCR 直接复制文字，之前在CSDN上找到影印版居然要12个资源分，嗯，我决定把分数挣回来

经典阵列处理教程：《Optimum_Array_Processing》的英文版，中文名《最优阵列处理技术》Harry_L._Van_Trees著

Behavior trees are used by AAA studios to create a lifelike AI. With Opsive's Behavior Designer, you can bring the power of behaviour trees to Unity! Behavior Designer is a behaviour tree implementation designed for everyone - programmers, artists, designers. Behavior Designer offers an intuitive visual editor with a powerful API allowing you to easily create new tasks. It also includes hundreds of tasks, PlayMaker integration, and extensive third party integration making it possible to create c

图像处理库-Mikolajczyk图像库.bark.bikes.boat.graf.leuven.trees.ubc.wall.包括.ppm格式和转换好的.png格式的图片数据集

For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.

KentaroToyama的 Wallflower paper中的图像测试集 原 paper：Kentaro Toyama, John Krumm, Barry Brumitt, Brian Meyers, "Wallflower: Principles and Practice of B