"Feature extraction"의 두 판 사이의 차이

수학노트
둘러보기로 가기 검색하러 가기
(→‎노트: 새 문단)
 
(→‎메타데이터: 새 문단)
61번째 줄: 61번째 줄:
 
===소스===
 
===소스===
 
  <references />
 
  <references />
 +
 +
== 메타데이터 ==
 +
 +
===위키데이터===
 +
* ID :  [https://www.wikidata.org/wiki/Q1026626 Q1026626]

2020년 12월 26일 (토) 05:23 판

노트

위키데이터

말뭉치

  1. Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set.[1]
  2. Over decades of research, engineers and scientists have developed feature extraction methods for images, signals, and text.[1]
  3. Automated feature extraction uses specialized algorithms or deep networks to extract features automatically from signals or images without the need for human intervention.[1]
  4. With the ascent of deep learning, feature extraction has been largely replaced by the first layers of deep networks – but mostly for image data.[1]
  5. Feature extraction involves reducing the number of resources required to describe a large set of data.[2]
  6. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy.[2]
  7. Feature extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing.[3]
  8. The process of feature extraction is useful when you need to reduce the number of resources needed for processing without losing important or relevant information.[3]
  9. Feature extraction can also reduce the amount of redundant data for a given analysis.[3]
  10. Feature extraction is a part of the dimensionality reduction process, in which, an initial set of the raw data is divided and reduced to more manageable groups.[4]
  11. So Feature extraction helps to get the best feature from those big data sets by select and combine variables into features, thus, effectively reducing the amount of data.[4]
  12. This brings us to the end of this article where we learned about feature extraction.[4]
  13. We can now repeat a similar workflow as in the previous examples, this time using a simple Autoencoder as our Feature Extraction Technique.[5]
  14. Feature extraction is a fundamental step for automated methods based on machine learning approaches.[6]
  15. Feature extraction algorithm: We now detail the systematic feature extraction procedure.[7]
  16. Feature extraction is very different from Feature selection : the former consists in transforming arbitrary data, such as text or images, into numerical features usable for machine learning.[8]
  17. An approach that seeks a middle ground between these two approaches to data preparation is to treat the transformation of input data as a feature engineering or feature extraction procedure.[9]
  18. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem.[10]
  19. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms.[10]
  20. In the image above, we feed the raw input image of a motorcycle to a feature extraction algorithm.[11]
  21. Let’s treat the feature extraction algorithm as a black box for now and we’ll come back to it soon.[11]
  22. Feature extraction is the process of determining the features to be used for learning.[12]
  23. Several studies targeted feature extraction in sEMG.[13]
  24. The code can easily reduce over 15 times the feature extraction computational time, which is related to the hardware.[13]
  25. The signal feature extraction scripts were used in previous works on sEMG data analysis and on kinematics data too.[13]
  26. The parallel signal feature extraction scripts were tested on sEMG data in this paper.[13]
  27. Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems.[14]
  28. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations.[14]
  29. In this work a novel event-based feature extraction method is proposed that focuses on these issues.[14]
  30. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view.[14]
  31. During feature extraction, uncorrelated or superfluous features will be deleted.[15]
  32. As a method of data preprocessing of learning algorithm, feature extraction can better improve the accuracy of learning algorithm and shorten the time.[15]
  33. Common methods of text feature extraction include filtration, fusion, mapping, and clustering method.[15]
  34. Traditional methods of feature extraction require handcrafted features.[15]
  35. Feature extraction is a quite complex concept concerning the translation of raw data into the inputs that a particular Machine Learning algorithm requires.[16]
  36. In general, a minimum of feature extraction is always needed.[16]
  37. However, things are not so clear when discussing feature extraction.[16]
  38. A feature extraction pipeline varies a lot depending on the primary data and the algorithm to use and it turns into something difficult to consider abstractly.[16]
  39. Agilent's feature extraction software automatically reads and processes up to 100 raw microarray image files.[17]
  40. In this tutorial, you will learn how to use Keras for feature extraction on image datasets too big to fit into memory.[18]
  41. Utilize Keras feature extraction to extract features from the Food-5K dataset using ResNet-50 pre-trained on ImageNet.[18]
  42. From there, the extract_features.py script will use transfer learning via feature extraction to compute feature vectors for each image.[18]
  43. After the feature extraction process, the data can be analysed.[19]
  44. In this tutorial, you will use Feature Extraction to extract rooftops from a multispectral QuickBird scene of a residential area in Boulder, Colorado.[20]
  45. Feature Extraction provides a quick, automated method for identifying rooftops, saving an urban planner or GIS technician from digitizing them by hand.[20]
  46. From the Toolbox, select Feature Extraction > Example Based Feature Extraction Workflow.[20]
  47. a Classification Method Feature Extraction offers three methods for supervised classification: K Nearest Neighbor (KNN), Support Vector Machine (SVM), or Principal Components Analysis (PCA).[20]
  48. Feature Extraction Features are user-defined objects that can be modeled or represented using geographic data sets.[21]
  49. Use Feature Extraction to identify objects from panchromatic or multispectral imagery based on spatial, spectral, and texture characteristics.[21]
  50. You must have an ENVI Feature Extraction license in order to use these tools and API routines.[21]
  51. Feature extraction is a process utilized in both machine learning and image processing by which data is transformed into a smaller more relevant set of data.[22]
  52. Feature extraction can be performed on texts as part of NLP or on images for computer vision tasks.[22]
  53. Some specific examples of types of algorithms often used in feature extraction are principle component analysis, and linear discriminant analysis.[22]
  54. Feature extraction is fundamental to many machine learning algorithms.[22]
  55. Image feature extraction is a necessary first step in using image data to control a robot.[23]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Feature Extraction
  2. ↑ 2.0 2.1 Feature extraction
  3. ↑ 3.0 3.1 3.2 Feature Extraction
  4. ↑ 4.0 4.1 4.2 What is Feature Extraction? Feature Extraction in Image Processing
  5. ↑ Feature Extraction Techniques
  6. ↑ Feature Extraction - an overview
  7. ↑ Feature Extraction - an overview
  8. ↑ 6.2. Feature extraction — scikit-learn 0.23.2 documentation
  9. ↑ How to Use Feature Extraction on Tabular Data for Machine Learning
  10. ↑ 10.0 10.1 An Introduction to Feature Extraction
  11. ↑ 11.0 11.1 The Computer Vision Pipeline, Part 4: feature extraction
  12. ↑ Introduction to Pattern Recognition and Machine Learning
  13. ↑ 13.0 13.1 13.2 13.3 PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows
  14. ↑ 14.0 14.1 14.2 14.3 Event-Based Feature Extraction Using Adaptive Selection Thresholds
  15. ↑ 15.0 15.1 15.2 15.3 Text feature extraction based on deep learning: a review
  16. ↑ 16.0 16.1 16.2 16.3 What is the difference between feature extraction and feature selection?
  17. ↑ image analysis software, Feature Extraction Software
  18. ↑ 18.0 18.1 18.2 Keras: Feature extraction on large datasets with Deep Learning
  19. ↑ Functional genomics II
  20. ↑ 20.0 20.1 20.2 20.3 Feature Extraction with Example-Based Classification Tutorial
  21. ↑ 21.0 21.1 21.2 Extract Features
  22. ↑ 22.0 22.1 22.2 22.3 Radiology Reference Article
  23. ↑ 13: Feature Extraction

메타데이터

위키데이터

원본 주소 "https://wiki.mathnt.net/index.php?title=Feature_extraction&oldid=47147"