Machine learning applied
Machine Learning applied to Rule-Based Machine Translation
... The hybrid architecture that we describe in this chapter consists of a rule-based core system that uses statistical modules for certain disambiguation tasks. As for the language pair in question, Spanish-Quechua, the ...
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8. Machine Learning Applied Artificial Intelligence
... • Goal: Produce model to classify new inputs • Examples: spam filter, fraud detection, … Classification. • Given: Training data (records) with[r] ...
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Machine learning applied to electoral night forecasting
... de Machine Learning, como clustering y PCA; con datos históricos de elecciones pasadas y las que se obtienen del recuento de votos durante las elecciones ...
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Operational Research and Machine Learning Applied to Transport Systems
... – A novel two-stages strategy to exploit machine learning to estimate if a par- ticular packing solution is feasible in a constant O(1) computational time: the first stage exploits the m[r] ...
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Machine Learning Applied to Airspeed Prediction During Climb
... Using Machine Learning and a set of examples, we have built models predicting the values (cas, M ) of a CAS/Mach speed ...the Machine Learning method, a cross-validation is ...a Machine ...
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Machine Learning Applied to Multi-Electron Events in Scintillator
... supervised machine learning algorithms to distinguish between one and two electron events, as well as determine the origin of the ...complete, machine learning will be an important data ...
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Machine Learning Applied to GRBAS Voice Quality Assessment
... Voice problems are routinely assessed in hospital voice clinics by speech and language therapists (SLTs) who are highly skilled in making audio-perceptual evaluations of voice quality. The evaluations are often presented ...
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Machine learning applied to an intelligent and adaptive robotic inspection station
... of machine learning tech- ...supervised Machine Learning (ML) techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, ...
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Accuracy and interpretability trade-offs in machine learning applied to safer gambling
... However, industry surveys and feedback clearly indicate that in order to enable wider adoption of such data-driven methods, industry and policy makers require a greater understanding of how machine learning ...
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Unsupervised machine learning applied to scanning precession electron diffraction data
... Abstract Scanning precession electron diffraction involves the acquisition of a two-dimensional precession electron diffrac- tion pattern at every probe position in a two-dimensional scan. The data typically comprise ...
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Machine Learning Applied to Positive Displacement Compressors and Expanders Performance Mapping
... 4.2 Extrapolation capabilities It has been shown that the ANN models lead to a generally higher accuracy in mapping the performance of the two scroll machines. However, it is important to assess the extrapolation ...
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Quantifying the Trenches: Machine Learning Applied to NFL Offensive Lineman Valuation
... Passing Differential Average pass with player minus average pass without Rushing Differential Average rush with player minus average rush without Snap Adjusted ALY 20 Snap-count-adju[r] ...
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Towards machine learning applied to time series based network traffic forecasting
... using Machine Learning (ML) techniques, it is possible to correlate external data with a time series model to improve the predictions and adjustment by targeting the atypical ...
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Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs
... intervals, applied to pens with pigs of heterogeneous BW; (3) a five-phase feed- ing plan, similar to the former plan but applied to pens with pigs of homogeneous BW; and (4) an eight- to nine-phase ...
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Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large sample datasets
... Related to result (iv), in all cases there were several basins with very low NSE values (this is also true for the benchmark models, which we will discuss in Sect. 3.2). Using catchment characteristics as static input ...
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5 Applied Machine Learning Theory
... 2.1.5 Hybrid systems In ［4］ , a payload based and a non-payload- based methodologies are proposed to identify P2P traffic. The payload-based approach uses heuristics such as well known port number, frequently observed ...
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Machine learning techniques for applied information extraction
... into Machine Learning models Besides unlabeled texts, existing expert decision systems, manually built taxonomies or written descriptions can hold useful information about the Information Extraction task in ...
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Discovery of Materials Through Applied Machine Learning
... 2]. Machine Learn- ing (ML), a branch of artificial intelligence, is a collection of statistical techniques that have been progressively developed since the 1950s and have attracted a growing interest in a myriad ...
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A Comparison of Machine Learning Classifiers Applied to Financial Datasets
... Trees learning, since the classification rate dropped to almost half of the original ...Trees learning was the technique that performed the best in terms of accuracy and also in terms of speed when compared ...
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Machine Learning techniques applied to the statistical properties of spin systems
... study machine learning on two sides: both applying it to detect features of physical problems, and applying physical tools to understand deep properties of the ...
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