machine learning


A survey of techniques for internet traffic classification using machine learning .
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The research community has begun looking for IP traffic classification techniques that do not rely on well knownTCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the

Deep machine learning -a new frontier in artificial intelligence research
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Mimicking the efficiency and robustness by which the human brain represents information has been a core challenge in artificial intelligence research for decades. Humans are exposed to myriad of sensory data received every second of the day and are somehow able

Machine learning approaches for the neuroimaging study of Alzheimers disease
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The disease currently affects about 5.3 million people in the US, and the number of victims will significantly increase in the near future without the development of therapeutics. AD was the seventh-leading cause of death across all ages in the US in 2006; it was the fifth-leading

Representational learning with extreme learning machine for big data
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Restricted Boltzmann Machines (RBM) and auto encoders, learns to represent features in a dataset meaningfully and used as the basic building blocks to create deep networks. This paper introduces Extreme Learning Machine based Auto Encoder (ELM-AE), which learns

Evaluation of feature representation and machine learning methods in grasp stability learning
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This paper addresses the problem of sensor-based grasping under uncertainty, specifically, the on-line estimation of grasp stability. We show that machine learning approaches can to some extent detect grasp stability from haptic pressure and finger joint information. Using M lACHINE- LEARNING RESEARCH spans almost four decades. Much of the research has been to define various paradigms, establish the relationships among them, and elaborate the algorithms that characterize them. Much less effort, relatively speaking, has been

Declarative Systems for Large-Scale Machine Learning .
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In this article, we make the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine learning task, or hardcoding new optimizations, we argue for the use of recursive queries to

Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence
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This is a book every modern control engineer should have on his/her reference bookshelf.

SystemMLs Optimizer: Plan Generation for Large-Scale Machine Learning Programs.
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SystemML enables declarative, large-scale machine learning (ML) via a high-level language with R-like syntax. Data scientists use this language to express their ML algorithms with full flexibility but without the need to hand-tune distributed runtime execution

Machine learning in the life sciences
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preparation of the data, in which the data preprocessing methods are applied. data mining, in which the prepared data is processed with ML techniques. evaluation of the discovered

Support vector machine concept-dependent active learning for image retrieval
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Relevance feedback is a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively learns a users desired output or query concept by asking the user whether

Tupleware: Distributed Machine Learning on Small Clusters.
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There is a fundamental discrepancy between the targeted and actual users of current analytics frameworks. Most systems are designed for the challenges of the Googles and Facebooks of the world petabytes of data distributed across large cloud deployments

A secure and practical mechanism of outsourcing extreme learning machine in cloud computing
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The enlarging volume and increasingly complex structure of data involved in applications makes Extreme Learning Machine (ELM) over large-scale data a challenging task. The paper presents a secure and practical mechanism for outsourcing ELM in cloud computing

Artificial intelligence as a control problem: Comments on the relationship between machine learning and intelligent control
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Ultimately, the problem of Artificial Intelligence (and thus of Neural Nets) comes down to that of making a sequence of decisions over time so as to achieve certain goals. AI is thus a control problem, at least in a trivial sense, but also in a deeper sense. This view is to be

Geneticsbased machine learning for rule induction: Taxonomy, experimental study and state of the art
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The classification problem can be addressed by numerous techniques and algorithms, which belong to different paradigms of Machine Learning . In this work, we are interested in evolutionary algorithms, the so-called Genetics-Based Machine Learning algorithms. In The training, maintenance, deployment, monitoring, organization and documentation of machine learning (ML) models in short model management is a critical task in virtually all production ML use cases. Wrong model management decisions can lead to poor

Poster: Machine Learning Based Code Smell Detection Through WekaNose
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Code smells can be subjectively interpreted, the results provided by detectors are usually different, the agreement in the results is scarce, and a benchmark for the comparison of these results is not yet available. The main approaches used to detect code smells are

Guest Editors Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1
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IN recent years, rapid developments in genomics and proteomics have generated a large amount of data. Often, drawing conclusions from these data requires sophisticated computational analyses. Bioinformatics, or computational biology, is the interdisciplinary

Accelerating the Machine Learning Lifecycle with MLflow.
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Abstract Machine learning development creates multiple new challenges that are not present in a traditional software development lifecycle. These include keeping track of the myriad inputs to an ML application (eg, data versions, code and tuning parameters)

Machine learning for multi-modality genomic signal processing
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One of the main challenges in computational biology is the revelation and interpretation of the rich genomic information underlying cancer biology. Revealing such information can help facilitate classification and prediction of cancers and responses to therapies. Genomic



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