空二叉樹 的英文怎麼說
中文拼音 [kōngèrchāshù]
空二叉樹
英文
empty binary tree-
The first part of this thesis describes the theory of rbf neural networks. the input space is thus divided into hyperrectangles organized into a regression tree ( binary tree ) by recursively partition the input space in two
第一部分從rbf網路出發，通過遞歸分割將輸入空間劃分為兩部分，從而將輸入空間變成一個用超矩形構成的回歸樹（二叉樹） 。This paper discusses the development of spatio - temporal data modeling in the spatio - temporal database and designes the two - level avl - tree spatio - temporal data model based on realms for spatio - temporal database system - stadbsii
本文討論了時空數據庫中時空建模技術的發展過程，同時為時空分析數據庫系統stadbsii設計了基於realms的二級平衡二叉樹模型。In this dissertation, the research trends for the problem have been introduced ; the ‘ dim ’ and ‘ point ’ has been strictly defined in mathematics from machine vision and human vision ; the ideal clutter suppression system based on clutter predication and the realization and evaluation of evaluation index has been studied, in succession the clutter suppression technologies have been researched. firstly, the classic nonparametric algorithm has been analyzed in detail and systematically, for it ’ s weakness that it cannot remove the non - stationary clutter ideally, kalman filter algorithm for clutter suppression in 2d image signal has been built. secondly, fast adaptive kalman filter is presented based on fast wide - sense stationary areas partition algorithm : limited combination and division algorithm based on quarti - tree algorithm, new taxis filter route algorithm which can break through the limitation of the necessity of pixel neighborhood of 2d filter and laplace data model with two parameters which is perfectly suitable for the residual image of kalman clutter suppression
首先分析了經典的非參數法，對於四種具有代表性的核，從前述的三個性能評價方面做了分析和對比，指出了其速度快的優點和對非平穩圖像適應性差的弱點，針對非參數法的弱點，重點研究了對非平穩圖像適應良好的卡爾曼雜波抑制技術:建立了非平穩圖像的類自回歸模型，在此基礎上建立了二維卡爾曼濾波基礎的兩個方程:狀態方程和測量方程;建立了非平穩圖像準平穩區域快速劃分演算法:基於四叉樹法的有限分裂合併演算法;二維空間的基於k排序的濾波路線演算法，突破了空域濾波路線上區域相鄰的限制;在這些研究的基礎上實現了快速卡爾曼估計，實驗驗證了該方法相對逐點卡爾曼估計可以提高運算速度三倍左右;雜波抑制結果表明傳統的高斯性檢驗並不適合卡爾曼估計后的殘余圖像，由此建立了殘余圖像的雙參數拉普拉斯模型，實驗表明其可以完好的吻合殘余圖像的概率密度曲線。Firstly, some basic algorithms for inducing decision tree are discussed, including id3, which uses information gain to select a splitting attribute when partitioning a training set ; c4. 5, which can deal with numeric attributes ; cart, which uses gini rule in attribute selection and induces a binary tree ; public, which puts tree pruning in the tree building phase ; interactive method, which puts artificial intelligence and human - computer interaction into the procedure of decision tree induction ; as well as sliq and sprint which are scalable and can be easily parallelized. advantages and disadvantages of these algorithms are also presented
文中詳細闡述了幾種極具代表性的決策樹演算法：包括使用信息熵原理分割樣本集的id3演算法；可以處理連續屬性和屬性值空缺樣本的c4 . 5演算法；依據gini系數尋找最佳分割並生成二叉決策樹的cart演算法；將樹剪枝融入到建樹過程中的public演算法；在決策樹生成過程中加入人工智慧和人為干預的基於人機交互的決策樹生成方法；以及突破主存容量限制，具有良好的伸縮性和并行性的sliq和sprint演算法。The spatio - tempral management subsystem stores, updates and searchs the spatio - temporal objects according to the two - level avl - tree data model
時空管理子系統根據二級平衡二叉樹模型來組織時空對象的存儲，更新，以及對所有時空對象進行檢索。The conventional ways has low efficiency, which result in the application of overlap grid in moving - grid problem, which need re - disposes the interpolating connection in every step in time direction, is very difficult considering the computational efficiency. aim at the above problem in application of overlap grid, this paper using an new manner rooting in analysissitus to put forward a “ method of distinguishing points of intersection ” and develop corresponding computational soft, which could realize the automatic and shape - independent pre - disposal process for overlap grid. to deal with the problem of low efficiency for pre - disposal process in moving - grid, this paper bring forward the “ mosaic ” method and adt method which based on the huge memory of modern computer
對于重疊網格在動網格應用中的計算效率問題，本文通過進一步研究插值信息的高速查詢方法，提出不同於傳統演算法思路，以現代計算機大內存為基礎，用計算機物理內存空間換計算時間的」 mosaic 」方法以及應用基於二叉樹數據結構的adt （ alternatingdigitaltree ）方法兩類方法，成功的將原處理時間提高了兩個數量級? ?即將網格規模在百萬量級的重疊網格預處理時間從傳統方法的耗時30 60分鐘提高到幾十秒鐘，從而成功解決了這一問題。To maintain high generalization ability, the most widespread class should be separated at the upper nodes of a binary tree
為了獲得較高的推廣能力，必須讓樣本分佈廣的類處於二叉樹的上層節點，才能獲得更大的劃分空間。分享友人