Classifier scalable

Laboratory instruments are classified into different categories according to their complexity to enable a scalable validation approach. The expected validation deliverables and documentation requirements for each class of laboratory instrument can be defined in the validation guide for the laboratory. 

The EZ-Track system can also be used to count good parts, diverted parts, packed cases, etc. Downtime is measured automatically and can be classified into an unlimited number of causes. Once production data is collected, there is an extensive set of Web-based reports that can incorporate trend charts, tabular reports, pie charts, and Pareto charts. Finally, it is possible to interface the EZ-Track system to ERP/MRP systems via an advanced SQL database that is open, fully documented, and ODBC-compliant. Not only is the EZ-Track system scalable from small to large numbers of molding machines and other types of cyclical manufacturing equipment, it also can play an important role in sending real-time production data to company-wide ERP/MRP systems. 

There are many classification tools available for use. Some examples are Oracle Text, ReelTwo s CS, and SVMLight. Oracle has several built into their OraclelOg database. SVMLight is available for SVM-based text classification [20]. ReelTwo [22] has a classifier with a user interface to ease development of categorization models. Most of the authors text-categorization experience is based on Oracle or ReelTwo. A variety of other classification applications are available. Selection of a specific application is dependent on classification goals, user interface, application features, and scalability requirements. 

A parallel algorithm is said to be scalable if its parallel efficiency can be maintained as the number of processes increases. Scalability may be classified as either weak or strong, depending on how the efficiency varies with the number of processes and the problem size. An algorithm is weakly scalable if the efficiency does not decrease as the number of processes grows, provided that the problem size increases as well. Thus, for process counts pi and p2 and problem sizes i and 2/ with p2 > pi and fi2 > fiu weak scalability implies that... 

Shafer, J., R. Agrawal, and M. Mehta. 1996. SPRINT A scalable parallel classifier for data mining. Pp. 544-555 in Proceedings of the 22nd International Conference on Very Large Databases, T. M. Vijayaraman, A. P. Buchmann, C. Mohan, and N. L. Sarda, eds. San Francisco Morgan Kaufmann. 

The framework of presented intelligent multi-sensor system is reflected by its data processing flow as illustrated in Fig. 3. Diversified sensors in field and sophisticated algorithms make the system scalable and adaptive to different driving profiles and scenarios. Data sets of complementary sensors are synchronized on the same time base before being conveyed to feature computation components. Based on the outcome of feature computation selected data sets are fused on the Mature level to construct input vectors for pattern classification so as to detect driver drowsiness. The classifier being used in this work is built upon Artificial Neural Network (ANN) or, more particularly. Multilayer Perceptrons (MLP) with supervised training procedure. 

P. Zappi, T. Stiefmeier, E. Farella, D. Roggen, L. Benini, and G. Troster, Activity recognition from on-body sensors by classifier fusion Sensor scalability and robustness, in Proc. ISSNIP, pp. 281-286, 2007. 

Recent advances in aerosol and combustion science and engineering now allow scalable flame synthesis of mixed oxides, metal salts, and even pure metals in the form of nanoparticles and films with closely controlled characteristics [71-76]. Flame processes are classified into vapor-fed and liquid-fed, depending on the employed state of the metal precursor [77]. Liquid-fed flame processes are distinguished for their flexibility in producing materials of various compositions and morphologies that result in unique product functionalities. 

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