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The evaluation software will be operational for a limited time only. Please contact your Nearest IAR

The evaluation software will be operational for a limited time only. Please contact your Nearest IAR Systems office or IAR Systems distributor if you want to purchase the full version of the product (contact link found below).

標簽： operational evaluation software contact

上傳時間： 2014-01-01

上傳用戶：Breathe0125
digital image interpolation techniques including Nearest neighbor, bilinear, bicubic and splin

digital image interpolation techniques including Nearest neighbor, bilinear, bicubic and spline interpolation.

標簽： interpolation techniques including bilinear

上傳時間： 2014-01-06

上傳用戶：小儒尼尼奧
data mining k Nearest neighbour

data mining k Nearest neighbour

標簽： neighbour Nearest mining data

上傳時間： 2017-09-15

上傳用戶：talenthn
利用高壓看門狗定時器加強汽車安全系統

Abstract: As electronic systems take over many of the mechanical functions in a car—ranging from engine timing to steering andbraking—there is a growing concern about fault tolerance. There should not be a single point of failure that would prevent a car fromat least "limping" off the road or making it to the Nearest service station. Redundant systems, watchdog timers, and other controlcircuits are used to reroute signals and perform other functions that ensure that a vehicle can safely make it off the road when afailure occurs.

標簽： 看門狗定時器汽車安全系統

上傳時間： 2013-11-10

上傳用戶：diets
Rainbow is a C program that performs document classification usingone of several different methods,

Rainbow is a C program that performs document classification usingone of several different methods, including naive Bayes, TFIDF/Rocchio,K-Nearest neighbor, Maximum Entropy, Support Vector Machines, Fuhr sProbabilitistic Indexing, and a simple-minded form a shrinkage withnaive Bayes.

標簽： classification different document performs

上傳時間： 2015-03-03

上傳用戶：希醬大魔王
物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA)

物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated facility location Vehicle routing: VRP, VRP with time windows, traveling salesman problem (TSP) Networks: Shortest path, min cost network flow, minimum spanning tree problems Geocoding: U.S. city or ZIP code to longitude and latitude, longitude and latitude to Nearest city, Mercator projection plotting Layout: Steepest descent pairwise interchange (SDPI) heuristic for QAP Material handling: Equipment selection General purpose: Linear programming using the revised simplex method, mixed-integer linear programming (MILP) branch and bound procedure Data: U.S. cities with populations of at least 10,000, U.S. highway network (Oak Ridge National Highway Network), U.S. 3- and 5-digit ZIP codes

標簽： location location-allocation Continuous alternate

上傳時間： 2015-05-17

上傳用戶：kikye
在visual basic環境下

在visual basic環境下，實現k-Nearest neighbor算法。

標簽： visual basic 環境

上傳時間： 2013-12-08

上傳用戶：ma1301115706
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters S

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the Nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.

標簽： the decision clusters Cluster

上傳時間： 2013-12-21

上傳用戶：gxmm
KNN算法的實現

KNN算法的實現，k-Nearest neighbors聚類算法的matlab 實現

標簽： KNN 算法

上傳時間： 2013-12-19

上傳用戶：AbuGe
樸素貝葉斯(Naive Bayes, NB)算法是機器學習領域中常用的一種基于概率的分類算法

樸素貝葉斯(Naive Bayes, NB)算法是機器學習領域中常用的一種基于概率的分類算法，非常簡單有效。k近鄰法(k-Nearest Neighbor, kNN)[30,31]又稱為基于實例(Example-based, Instance-bases)的算法，其基本思想相當直觀：Rocchio法來源于信息檢索系統，后來最早由Hull在1994年應用于分類[74]，從那以后，Rocchio方法就在文本分類中廣泛應用起來。

標簽： Naive Bayes NB 貝葉斯

上傳時間： 2014-01-03

上傳用戶：wxhwjf

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Nearest

The evaluation software will be operational for a limited time only. Please contact your Nearest IAR

digital image interpolation techniques including Nearest neighbor, bilinear, bicubic and splin

data mining k Nearest neighbour

利用高壓看門狗定時器加強汽車安全系統

Rainbow is a C program that performs document classification usingone of several different methods,

物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA)

在visual basic環境下

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters S

KNN算法的實現

樸素貝葉斯(Naive Bayes, NB)算法是機器學習領域中常用的一種基于概率的分類算法