搜索资源列表
tree
- 在线算大，用树套树解决在某区间内部求第K大的数-Line for a major, with tree tree cover to resolve the internal demand in a range of large number of the first K
knn
- 实现构建k-d树并且实现k-d树上的最邻近查找算法-Build a kd-tree and kd tree nearest neighbor search algorithm
multi-task_lasso
- MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso propo
KD_Tree
- 基于K维空间索引的点查询算法KD树，本代码实现一般的查询功能-K-dimensional space-based index point KD tree search algorithm, the code search function in general
poj2104.cpp
- 算法程序设计题 POJ 2104 ,使用主席树的数据结构来查询区间第K小的数-Algorithm design problem POJ 2104, the Chairman of the tree data structure to use to query a small number of K Interval
KMeansAlgorithm
- 主要用于实现数据挖掘中聚类算法中的一个经典算法，目的是将一组树按照就近原则分成k组-Mainly used for data mining clustering algorithm, a classical algorithm, the purpose is to set the tree into k groups according to the principle of proximity
KD-tree
- kd树是一种对k维空间中的实例点进行存储以便对其进行快速搜索的树形数据结构-kd tree is a k-dimensional space for instance points to be stored quickly search tree data structure
classification-Python
- python实现感知器、贝叶斯分类、决策树分类、K最近邻法、逻辑回归、支持向量机-Python implementation of perceptron, Bias classification, decision tree classification, K nearest neighbor method, logic regression, support vector machine
tree
- 判断二叉树是否是完全二叉树，求二叉树宽度1：若无左子女则不应该有右子女2.求二叉树宽度3.二叉树k层叶子结点-Determine whether the binary tree is a complete binary tree, binary tree width 1: children should not be left if no children have the right width 3. 2. binary tree binary tree leaf node k layer
K-value-problems-in-the-cod
- 有向树K中值问题，王晓东版算法课后题，有注释，很详细-Directed tree K value problems, Wang Xiaodong version algorithm after-school title, notes, very detailed
HaffmanTree
- 在普通哈夫曼树的基础上，实现K叉哈夫曼树-On the basis of the ordinary Huffman tree based on the realization K-ary Huffman
knn-kdtree
- kd树，分割k维数据空间的数据结构。主要应用于多维空间关键数据的搜索（如：范围搜索和最近邻搜索）。K-D树是二进制空间分割树的特殊的情况。-KD tree, the data structure of K dimensional data space. It is mainly used in the search of key data in multidimensional space (such as range search and nearest neighbor search). K
KD-tree
- C++构建简单的KD树并查找K近邻，是一篇文档，内附代码，代码亲测可用(C++ to build a simple KD tree, and find K nearest neighbor, is a document, enclosing code, code pro test available)
类比法
- 型的类比学习方法是K-最近邻方法,它属于懒散学习法,相比决策树等急切学习法,具有训练时间短,但分类时间长的特点。K-最近邻算法可以用于分类和聚类中(The analogy learning method is K- nearest neighbor method. It belongs to the lazy learning method. Compared with the decision tree learning method, it has the characteristics o
K d tree in matlab
- k d clustering in matlab
pres
- 三种分类器：决策树分类器，k-NN分类器和k-means分类器的运行时间以及运行准确率的比较。(Three kinds of classifiers: decision tree classifier, k-NN classifier and K-means classifier running time and accuracy comparison.)
QRD_MQAM
- 使用K-BEST树型搜索算法和QR分解，检测信号(Detection of signals using the K-BEST tree search algorithm and QR decomposition)
3-18有向树k值
- 要求有向树Ｔ的k个顶点组成的集合Ｆ，使cost(F)=∑min w(x)*d(x,u)的值达到最小。在一般情况下，有向树是一棵多叉树，为了便于计算，我们把多叉树变转成为与之等价的二叉树，将每个顶点的第一个儿子做为其父顶点的左儿子顶点，同时增加一个０权０边长的附加顶点做为右儿子顶点。然后对于其他儿子项点以相同方式作为新增附加顶点的左儿子顶点，一直继续下去，直至处理完所有顶点。所得到的二叉树与树Ｔ具有相同的最小耗费。(A set F that consists of the k vertices o
3-19有向树独立K
- 要求有向树Ｔ的k个独立顶点组成的集合Ｆ，使cost(F)=∑min w(x)*d(x,u)的值达到最小。与有向树k中值问题类似，把有向树变转成为与之等价的二叉树，设Ｔ的以顶点x为根的子树T(x),其左、右儿子顶点分别为y和z。(A set F that consists of a k independent vertex to a tree T is required to minimize the value of the cost (F) = min w (x) *d (x, U). Sim
create_kd_tree
- 通过计算方差，确定划分坐标轴，最后，将点云划分到一个个格子中，这样的好处在于可以将噪点和有效点分别存储于不同的格子中，方便进行去噪。(k-d tree create;By calculating the variance, the coordinate axis is determined. Finally, the point cloud is divided into a number of lattices. The advantage is that the noise and the