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"This volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike. "

"The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses."

"They present new and innovative developments and applications, divided into technical stream sections on data mining, data mining and machine learning, planning and optimisation, and knowledge management and prediction, followed by application stream sections on language and classification, recommendation, practical applications and systems, and data mining and machine learning. The volume also includes the text of short papers presented as posters at the conference."

"Data mining is one of the most rapidly growing research areas in computer science and statistics. Areas of application covered are diverse and include healthcare and finance. We wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field."

"Learning in non-stationary environments : methods and applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations."

"Menurut EEB Laboratory Jakarta, pada tahun 2016 sektor bangunan memiliki mengkonsumsi 18-20% dari total penggunaan energi di Indonesia, dan terus menerus meningkat seiring perkembangan teknologi yang membutuhkan sumber energi dalam upaya peningkatan kualitas hidup penghuni bangunan. Bangunan pintar merupakan sebuah konsep pemanfaatan teknologi yang tidak hanya bertujuan meningkatkan kenyamanan penghuni, tetapi juga dapat membantu dalam upaya efisiensi energi pada operasional bangunan. Maka dari itu, penelitian ini akan membantu upaya perancangan efisiensi energi pada sebuah bangunan dengan meninjau fitur dan karakteristik yang berpotensi dalam mendukung efisiensi energi dengan penerapan konsep bangunan pintar. Selain itu, akan dibuat sebuah model dengan pemanfaatan machine learning yang mampu memberikan prediksi tingkat penggunaan energi berdasarkan fitur-fitur yang diberikan. Model machine learning yang dihasilkan memiliki rata-rata nilai kesalahan relatif sebesar 17,76%, serta didapatkan tingkat efisiensi dengan penerapan seluruh fitur yang diidentifikasi pada rentang 34,5% hingga 45,3% tergantung pada lantai yang ditinjau.

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According to EEB Laboratory Jakarta, Indonesian building sector accounts for 18- 20% energy consumption in 2016, and this trend will continuously increase as technology needed to increase housing residents' quality keeps advancing. Smart building is a concept to utilise technology that does not only help increase occupants' comfort inside the building, but it can also help increase energy usage efficiency in building operations. This research aims to help the effort in designing energy efficiency planning for a building by reviewing potential features and characteristics that could help improves energy efficiency with implementation of the smart building concept. A model based on machine learning that could give prediction on the level of energy consumption based on given features will also be discussed here. This model of machine learning has a 17,76% average of relative error, as well as 34,5% until 45,3% efficienct level that includes implementation of all features, depending on analysed floor."

"This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings."

"Meskipun kajian mengenai bahan bakar dan penggerak alternatif sudah banyak, namun target dan aplikasinya dalam pengurangan emisi CO2 di pelabuhan masih kurang mendapat perhatian terutama di negara berkembang seperti Indonesia. Penelitian ini menggunakan machine learning dalam memperkirakan emisi CO2 dari aktivitas kapal di tujuh pelabuhan di Indonesia kemudian dicari variable yang berpengaruh pada total emisi sebagai fokus dalam pengembangan Ship Energy Efficiency Management Plan (SEEMP). Dilakukan prediksi total emisi CO2 menggunakan regresi hutan acak kemudian keefektifannya diverifikasi menggunakan validasi silang k-fold, hasil prediksi kemudian dibandingkan dengan total emisi perhitungan metode bottom-up. Hasil analisis attribute weight berdasarkan correlation menunjukkan bahwa daya mesin dan waktu operasi kapal di pelabuhan memiliki pengaruh yang lebih besar dalam menghasilkan emisi CO2. Prediksi total emisi menunjukkan bahwa model memiliki akurasi yang cukup rendah akibat banyaknya data yang kosong meskipun algoritma model sudah tergolong bagus. Akhirnya, operasi hemat bahan bakar dibahas dengan fokus pada tenaga dan bahan bakar alternatif serta peningkatan efisiensi kerja, penggunaan bahan bakar bersih dari hidrogen dan biofuel mamiliki potensi pengurangan yang paling tinggi dengan cold ironing sebagai alternatif yang dapat memenuhi syarat pengurangan emisi per tahun sebesar 20%. Dibutuhkan data yang lengkap untuk melakukan prediksi total emisi yang akurat serta pengembangan teknis dan ketersediaan sumber daya pada metode yang telah dibahas agar dapat di implementasikan kedalam Rencana Pengelolaan Efisiensi Energi Kapal.

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Although there are many studies on alternative fuels and drivers, the target and their application in reducing CO2 emissions at ports have received little attention, especially in developing countries such as Indonesia. This study uses machine learning to estimate CO2 emissions from ship activities at seven ports in Indonesia and then looks for variables that affect total emissions as a focus in developing a Ship Energy Efficiency Management Plan (SEEMP). Total CO2 emissions were predicted using random forest regression, their effectiveness was then verified using k-fold cross-validation, the prediction results were then compared with the total emissions calculated using the bottom-up method. The results of attribute weight analysis based on correlation show that engine power and ship operating time in port have a greater influence in producing CO2 emissions. Prediction of total emissions shows that the model has a fairly low accuracy due to the large number of blank data despite the model algorithm exelency. Finally, fuel-efficient operations are discussed with a focus on alternative power and fuels as well as improving work efficiency, the use of clean fuels from hydrogen and biofuels has the highest reduction potential with cold ironing as an alternative that can meet the requirements of 20% annual emission reduction. Complete data is needed to make accurate predictions of total emissions as well as technical development and resource availability on the methods discussed so that they can be implemented into the Ship Energy Efficiency Management Plan."

"This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns, Bayesian learning and graphical models, classification, dimensionality reduction, feature selection and extraction, distance-based methods and kernels, ensemble methods, graph and tree mining, large-scale, distributed and parallel mining and learning, multi-relational mining and learning, multi-task learning, natural language processing, online learning and data streams, privacy and security, rankings and recommendations, reinforcement learning and planning, rule mining and subgroup discovery, semi-supervised and transductive learning, sensor data; sequence and string mining, social network mining, spatial and geographical data mining, statistical methods and evaluation, time series and temporal data mining, and transfer learning."