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  • Machine Learning and Knowledge Discovery in

    This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions.

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  • ML-KNN: A lazy learning approach to multi-label

    2007-7-1 · Fig. 1 gives the complete description of M L-KNN. T is the training set as shown in Section 2 and the meanings of the input arguments K, t and the output argument y ⇒ t are the same as described previously. Furthermore, the input argument s is a smoothing parameter controlling the strength of uniform prior (In this paper, s is set to be 1 which yields the Laplace smoothing).

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  • Text Classification with Active Learning | SpringerLink

    Abstract. In many real world machine learning tasks, labeled training examples are expensive to obtain, while at the same time there is a lot of unlabeled examples available. One such class of learning problems is text classification. Active learning strives to reduce the required labeling effort while retaining the accuracy by intelligently ...

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  • Issue 87 | DeepLearning.AI

    2021-4-14 · The iterative aspect of machine learning applies to many steps. For example: Data labeling: It’s hard to come up with fully fleshed-out labeling guidelines that result in clean and consistent labels on your first attempt. It might be better to use an initial set of guidelines to label some data, see what problems arise, and then improve the guidelines.

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  • Multi-instance multi-label learning - ScienceDirect

    2012-1-1 · P. Auer, On learning from multi-instance examples: Empirical evaluation of a theoretical approach, in: Proceedings of the 14th International Conference on …

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  • What does depth bring to Machine Learning? – Intel ...

    Abstract. Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through ...

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  • ML-KNN: A lazy learning approach to multi-label

    2021-2-25 · Yu Yang, Enhong Chen, Qi Liu, Biao Xiang, Tong Xu, Shafqat Ali Shad, On Approximation of Real-World Influence Spread, In Proceedings of the 2012 European Conference on Machine Learning and Principles and Practice of Knowledge([PDF]

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  • Kaggle: Your Machine Learning and Data Science

    2016-1-1 · We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by ...

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  • Machine Learning Research Group | University of Texas

    2021-6-15 · In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), 406-414, Bled, Slovenia, June 1999. Semantic Lexicon Acquisition for Learning Natural Language Interfaces Cynthia Ann Thompson PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, December 1998. 101 pages.

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  • Machine Learning and Knowledge Discovery in

    This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected

    Get Price
  • Infosys Knowledge Institute | Scaling AI: Data Over

    2021-5-24 · Machine learning models have generated much hype. But without clean, labeled data, their outcomes are flawed. Humans have traditionally been used to do the labeling, but bias can creep in, and costs often escalate. Instead, a combination of intelligent learners and a programmatic data creation approach is required.

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  • Juš Kosmač - Slovenia | Professional Profile | LinkedIn

    2018-3-14 · In 16th International Conference on Machine Learning, Bled, Slovenia, 1999; 200–9. Chapelle O and Zien A. Semi-supervised learning by low density separation. In 10th International Workshop on Articial Intelligence and Statistics, Barbados, 2005; 57–64.

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  • MaxQ AI Selects Flywheel for Machine Learning

    The aim of the internship was to familiarize students with possible uses of machine learning in various industrial environments. Several lectures on basic machine learning algorithms were also included. The work was focused on: - image labeling, pre-processing and transformation - crack detection on images of concrete blocks using neural networks

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  • Machine Learning: Time based anomaly detection |

    2019-12-16 · December 16, 2019 — MaxQ AI, a company focused on developing artificial intelligence (AI) applications to enable doctors to reach faster, more accurate decisions when diagnosing stroke, traumatic brain injury, and other life-threatening conditions, has selected Flywheel to support data management and curation and gain efficiencies in their machine learning workflow.

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  • Simulated iterative classification a new learning ...

    2020-3-17 · Machine learning: Time-based anomaly detection (part 3) Anomaly detection is a much-appreciated tool by data scientists. It aims to find data samples that do not conform to the regular distribution of the dataset to which they belong. Finding anomalous samples, also known as distribution outliers, provides valuable insight that often correlates ...

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  • Visual OntoBridge | Proceedings of the 2009th

    Simulated iterative classification a new learning procedure for graph labeling. Share on. Authors: Francis Maes. LIP6 - University Pierre et Marie Curie, Paris, France. LIP6 - University Pierre et Marie Curie, Paris, France. View Profile,

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  • 1st Freely Available Machine Learning (ML) Life Cycle ...

    2020-12-4 · The whitepaper significantly contributes to our understanding of the development processes in machine learning. The key element of this research work is its examination of the iterative labeling process by which the machine learning model improves itself. This significantly reduces the efforts and costs incurred by manual data labeling.

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  • Automaton AI: ADVIT - Deep Learning Platform (white ...

    It is a cost-effective data labeling tool (Reduce AI development cost by 2x, Zero start-up cost). ADVIT key features: 1. Hierarchical Attribute Tagging 2. Deep Learning model integration to speed up the annotation process (Automated Labeling) 3. Self-hosted data labeling tool 4. Expert data annotators ADVIT value adds to the data-labeling ...

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  • Toloka Expands Data Labeling Service - Datanami

    2021-4-27 · Data-labeling crowdsourcing platforms have become popular in recent years as organizations scramble to provide large amounts of labeled data for large neural networks. Unlike traditional machine learning algorithms, deep learning systems, such as those used for computer vision and textual processing workloads, require huge volumes of data.

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  • Infosys Knowledge Institute | Scaling AI: Data Over

    2021-5-24 · Machine learning models have generated much hype. But without clean, labeled data, their outcomes are flawed. Humans have traditionally been used to do the labeling, but bias can creep in, and costs often escalate. Instead, a combination of intelligent learners and a programmatic data creation approach is required.

    Get Price
  • Ebook: How to Improve Data Quality With Data Labeling

    2021-6-11 · Adopt an Efficient Data Labeling Process. Data needs to be valuable (high quality, labeled, and organized) to drive machine learning model success. This ebook discusses the importance of data quality in any end-to-end AI project, with a specific focus on the need for data labeling through active learning.

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  • Text Classification with Active Learning | SpringerLink

    One such class of learning problems is text classification. Active learning strives to reduce the required labeling effort while retaining the accuracy by intelligently selecting the examples to be labeled. However, very little comparison exists between different active learning methods. The effects of the ratio of positive to negative examples ...

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  • Machine Learning Research Group | University of Texas

    Active learning differs from passive 'learning from examples' in that the learning algorithm itself attempts to select the most informative data for training. Since supervised labeling of data is expensive, active learning attempts to reduce the human effort needed to learn an accurate result by selecting only the most informative examples for labeling.

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  • Active learning for sentiment analysis on data streams ...

    2015-10-28 · Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform Janez Kranjca,b,⇑, Jasmina Smailovic´ a,b, Vid Podpecˇana,c, Miha Grcˇara, Martin Zˇnidaršicˇa, Nada Lavracˇa,b,d a Jozˇef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia bJozˇef Stefan International Postgraduate School, Jamova cesta …

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  • Anomaly Detection for Time Series Data with Deep

    2017-2-11 · Machine learning has long powered many products we interact with daily–from 'intelligent' assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new ...

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  • COPERNICUS & MACHINE LEARNING Collaboration of

    2020-11-5 · Founded in 1949, JSI is the largest research institute in Slovenia. Physics, chemistry, molecular biology, biotechnology, ... AI, Machine Learning and Deep Learning and Archaeology ... • Labeling/annotation efforts

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  • Automaton AI: ADVIT - Deep Learning Platform (white ...

    It is a cost-effective data labeling tool (Reduce AI development cost by 2x, Zero start-up cost). ADVIT key features: 1. Hierarchical Attribute Tagging 2. Deep Learning model integration to speed up the annotation process (Automated Labeling) 3. Self-hosted data labeling tool 4. Expert data annotators ADVIT value adds to the data-labeling ...

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  • AWS Marketplace: Land Cover data (Slovenia/Europe)

    Machine Learning Human Review Services ML Solutions Data Labeling Services Computer Vision Natural Language Processing Speech Recognition Text Image Video Audio Structured Intelligent Automation Data Products Financial Services Data Healthcare & Life Sciences Data Media & Entertainment Data Telecommunications Data Gaming Data Automotive Data ...

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  • Machine learning for medical diagnosis: history, state

    2001-8-1 · Machine learning technology is currently well suited for analyzing medical data, and in particular there is a lot of work done in medical diagnosis in small specialized diagnostic problems. Data about correct diagnoses are often available in the form of medical records in …

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  • Machine Learning: Time based anomaly detection |

    2020-3-17 · Machine learning: Time-based anomaly detection (part 3) Anomaly detection is a much-appreciated tool by data scientists. It aims to find data samples that do not conform to the regular distribution of the dataset to which they belong.

    Get Price
  • Data Collection and Labeling Market Size, Share &

    Data Collection and Labeling Market Share. Data labeling is the manual solution for machine learning and AI applications data by humans. Labeling data is important because computers have endless shortcomings and some of them can't be overcome easily without human intervention.

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  • Practical Data Science (PDS) Specialization |

    Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.

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  • US: FDA Releases Artificial Intelligence/Machine

    2021-1-19 · The FDA has released an action plan to develop a regulatory framework on artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). The FDA aims to publish the draft guidance this year. As a Digital Research Organization, Meditrial understands the industry and how to make the most of advanced technologies and is ...

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  • How to Build a Machine Learning Mobile App with

    2018-11-30 · We’ve Just Built a Machine Learning App with Kinvey. That’s how quickly we can build a mobile app that utilizes machine learning. Combining Kinvey with NativeScript we can build a powerful and incredibly fast application with relatively little code. Kinvey has great documentation here to help you get started with NativeScript, but you can also try a different platform as per your requirements.

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  • HeadKount, Inc. hiring Machine Learning Engineer in

    Posted 7:37:03 PM. We are looking for a machine learning engineer that is motivated to transform an industry. The…See this and similar jobs on LinkedIn.

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  • COPERNICUS & MACHINE LEARNING Collaboration of

    2020-11-5 · Founded in 1949, JSI is the largest research institute in Slovenia. Physics, chemistry, molecular biology, biotechnology, ... AI, Machine Learning and Deep Learning and Archaeology ... • Labeling/annotation efforts

    Get Price
  • Cognex | Machine Vision and Barcode Readers

    Cognex Deep Learning is the first set of field-tested, optimized, and proven inspection technology based on state-of-the-art machine learning algorithms. Combining artificial intelligence (AI) with In-Sight or VisionPro software, it automates and scales complex and challenging inspection applications.

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  • Machine learning for medical diagnosis: history, state

    2001-8-1 · Machine learning technology is currently well suited for analyzing medical data, and in particular there is a lot of work done in medical diagnosis in small specialized diagnostic problems. Data about correct diagnoses are often available in the form of medical records in …

    Get Price
  • Machine Learning: Time based anomaly detection |

    2020-3-17 · Machine learning: Time-based anomaly detection (part 3) Anomaly detection is a much-appreciated tool by data scientists. It aims to find data samples that do not conform to the regular distribution of the dataset to which they belong.

    Get Price
  • 5 Reasons You Don’t Need to Learn Machine Learning

    2020-11-26 · Senior Machine Learning engineers don’t earn more than other Senior engineers (at least not in Slovenia). There are some Machine Learning superstars in the US, but they were in the right place at the right time — with their mindset. I’m sure there are Software Engineers in the US who have even higher wages. 4. Machine Learning is future proof

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  • Seminar: Machine Learning (Summer Term 2018) –

    Seminar: Machine Learning (Summer Term 2018) The aim of this practical seminar (7 CP) is the realization of a complete pipeline of a project from the problem statement to finding solutions using methods of machine learning on our Deep Learning cluster. The topics are proposed by different groups of the Mathematics and Computer Science ...

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  • Machine Learning Companies | ML Services And

    Machine learning algorithms, frameworks, and techniques are embraced by a wide number of businesses to increase customer satisfaction, efficiency, and performance. AI and machine learning solutions can be implemented in almost every sector including retail, healthcare, Industry 4.0, …

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  • Semantic Robots – Machine Perception and Interaction

    Semantic Robots – Machine Perception and Interaction. Semantic Robots is a strategic partnership between the Centre for Applied Autonomous Sensor Systems, which MPI is a part of, and several industrial partners, all leaders in their respective fields. This is a 6-year research profile funded by The Knowledge Foundation for a total of 36MSEK ...

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  • Inorsa, Inc. hiring Machine Learning Engineer in Austin ...

    As a Lead Machine Learning Engineer at Inorsa, you will contribute to innovative technology which supports major telecom 5G deployment projects in the nation, with all the scalability and high ...

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  • Interactive Data Exploration and Analytics (IDEA 2015 ...

    2020-8-22 · The Interactive Data Exploration and Analytics (IDEA) workshop addresses the development of data mining techniques that allow users to interactively explore their data.We focus and emphasize on interactivity and effective integration of techniques from data mining, visualization and human-computer interaction (HCI).In other words, we explore how the best of these different but related domains ...

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