Download JAR files tagged by category with all dependencies

OpenCms-Module 'org.opencms.workplace.categories'. <p>This module contains the OpenCms Workplace category folders.</p> <p>It includes the system wide category folders.</p> <p><i>(c) 2012 by Alkacon Software GmbH (http://www.alkacon.com).</i></p> OpenCms is a Content Management System that is based on Open Source Software. Complex Intranet and Internet websites can be quickly and cost-effectively created, maintained and managed.

Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that "most contains the instance". Does not handle numeric class, or missing values in test cases. Extremely simple algorithm, but has the advantage of being extremely fast, and works quite well when you have "smegloads" of attributes.

EnOS Device And Asset API pojo 0.2.14-SNAPSHOT ota api pojo and event 0.2.20-SNAPSHOT The Response objects returned by GetEvent and SearchEvent queries are added (including: outputData, eventName, outputNames fields) 0.2.21-SNAPSHOT Increase the internationalization of the four-element description field of the model 0.2.22-SNAPSHOT add some pojo class for private api 0.2.23-SNAPSHOT model-service open api的接口中thingModel增加category模型类型字段 0.2.24-SNAPSHOT 部分search接口增加realTime字段 0.2.25-SNAPSHOT firmware add tag function 0.2.27-SNAPSHOT add max and min fields 0.2.28-SNAPSHOT asset tree api for amc/city infra 0.2.29-SNAPSHOT fix thingModel enableMeasurepointValidate 0.2.30-SNAPSHOT enhancement of search model and update asset and alert engine 增加告警规则sdk 0.2.31-SNAPSHOT move asset node 2.4.0-SNAPSHOT upgrade to 2.4 for support DCM 2.4GA 2.4.1-SNAPSHOT service parameter supports "Required" and "Default Value" fields 2.4.2-SNAPSHOT upgrade to 2.4 for support DCM 2.4GA jar 升级 2.4.3-SNAPSHOT add alert-engine v2.5 alertRule openAPI

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.