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"The Lanczos Method: Evolution and Application is divided into two distinct parts. The first part reviews the evolution of one of the most widely used numerical techniques in the industry. The development of the method, as it became more robust, is demonstrated through easy-to-understand algorithms. The second part contains industrial applications drawn from the author experience. These chapters provide a unique interaction between the numerical algorithms and their engineering applications."

"There has been an explosive growth in the field of combinatorial algorithms. These algorithms depend not only on results in combinatorics and especially in graph theory, but also on the development of new data structures and new techniques for analyzing algorithms. Four classical problems in network optimization are covered in detail, including a development of the data structures they use and an analysis of their running time. Data Structures and Network Algorithms attempts to provide the reader with both a practical understanding of the algorithms, described to facilitate their easy implementation, and an appreciation of the depth and beauty of the field of graph algorithms."

"We were very pleased to choose papers for presentation at the ACM-SIAM Symposium on Discrete Algorithms (SODA), which will take place in Manhattan in January 2009. A total of 550 short abstracts were submitted, later materializing into 458 submissions, of which 135 were selected. The program committee meeting was entirely electronic and the selection process took place in July and August 2008. The Best Student Paper award was given to the paper "Improved Bounds and New Techniques for Davenport?Schinzel Sequences and Their Generalizations" by Gabriel Nivasch, and the Best Paper award was given to the paper "Natural Algorithms" by Bernard Chazelle. There will be three invited presentations: one by Volker Strassen, as the recipient of the ACM-SIGACT 2008 Knuth prize; one by Michael Jordan; and one by Yuval Peres."

"The papers in this volume were presented at the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, held January 7/9, 2007, in New Orleans, Louisiana. The Symposium was jointly sponsored by the SIAM Activity Group on Discrete Mathematics and by SIGACT, the ACM Special Interest Group on Algorithms and Computation Theory."

"ABSTRAKKlasifikasi merupakan proses pengelompokan suatu himpunan data ke kelas-kelas yang sudah ada sebelumnya. Pada umumnya, himpunan data dibagi menjadi dua bagian, yaitu training data dan testing data. Dibutuhkan suatu metode klasifikasi yang dapat mengelompokkan training data dan testing data ke dalam suatu kelas dengan tepat. Sering kali metode klasifikasi hanya dapat mengelompokkan training data dengan tepat saja, namun tidak demikian untuk testing data. Artinya, model yang terbentuk tidak cukup stabil atau model tersebut mengalami overfitting. Secara umum, overfitting merupakan kondisi saat akurasi yang dihasilkan pada training data cukup tinggi, namun cenderung tidak mampu memprediksi testing data. Penentuan metode klasifikasi yang rentan terhadap overfitting perlu dipertimbangkan. Random forest merupakan salah satu metode klasifikasi yang rentan terhadap masalah overfitting. Hal tersebut sekaligus menjadi salah satu kelebihan dari metode random forest. Oleh karena itu, pada tugas akhir ini akan dibahas metode random forest serta mengaplikasikannya pada data penderita penyakit Parkinson yang dibagi berdasarkan 2 sub-tipe, yaitu tremor dominant TD dan postural instability gait difficulty PIGD dominant. Selanjutnya, dari data tersebut diperoleh hasil akurasi model yang dihasilkan dalam mengklasifikasi training data, yaitu sekitar 94,25 . Sementara itu, akurasi metode ini dalam melakukan klasifikasi pada data yang tidak terkandung dalam membentuk model sebesar 94,26.

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ABSTRACTClassification is the process of grouping a set of data into pre existing classes. In general, the data set is divided into two parts. There are training data and testing data. It takes a classification method that can classify both training data and testing data of its class appropriately. However, some of the classification methods only fit in training data, but it can not apply in testing data. It means that the model is unstable or the model occurs overfitting. In general, overfitting is a condition when the model too fit in training, but unable to predict testing data. In other words, the accuracy of predicting the testing data is decreasing. Therefore, the determination of classification methods that are vulnerable to overfitting need to be considered. Random forest is one of the classification methods that is vulnerable to overfitting. It is also one of the advantages of the random forest method. Therefore, in this final project will be discussed random forest method and applying it to the data of Parkinson 39 s disease patients that is divided by 2 sub types. There are dominant tremor TD and postural instability gait difficulty PIGD dominant. Furthermore, from the data obtained the results of model accuracy in classifying the training data is about 94.25 . Meanwhile, the accuracy of this method in classifying the data not contained in forming a model is about 94.26."

"The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework.Matlab codes and some benchmark structural optimization problems are provided. The aim is to provide an efficient context for experienced researchers or readers not familiar with theory, applications and computational developments of the considered metaheuristics.The information will also be of interest to readers interested in application of metaheuristics for hard optimization, comparing conceptually different metaheuristics and designing new metaheuristics."

"This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Workshop on Algorithms for Sensor Systems, Wireless Ad Hoc Networks, and Autonomous Mobile Entities, ALGOSENSORS 2011, held in Saarbrücken, Germany, in September 2011. The 16 revised full papers presented together with two invited keynote talks were carefully reviewed and selected from 31 submissions. The papers are organized in two tracks: sensor networks, covering topics such as localization, lifetime maximization, interference control, neighbor discovery, self-organization, detection, and aggregation; and ad hoc wireless and mobile systems including the topics: routing, scheduling and capacity optimization in the SINR model, continuous monitoring, and broadcasting."