1. What is pattern recognition in the context of computer science?
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In computer science, pattern recognition is the automated recognition of patterns and regularities in data using algorithms and statistical techniques.
2. What is a common application of pattern recognition?
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Pattern recognition is widely used in image processing and computer vision for tasks like object recognition, face detection, and automated driving systems.
3. What is 'feature extraction' in pattern recognition?
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Feature extraction involves reducing the number of resources required to describe large sets of data accurately. It transforms raw data into a set of features that are more meaningful and informative for analysis.
4. What does a 'confusion matrix' show in pattern recognition?
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A confusion matrix is a table often used to describe the performance of a classification model on a set of test data for which the true values are known.
5. What is 'supervised learning' in the context of pattern recognition?
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Supervised learning in pattern recognition involves learning a function that maps input to output based on example input-output pairs. It infers a function from labeled training data.
6. What is 'unsupervised learning' in pattern recognition?
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Unsupervised learning is a type of algorithm that learns patterns from untagged data. The system tries to learn without a teacher by finding structures in the input data.
7. What is the primary goal of a 'neural network' in pattern recognition?
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Neural networks in pattern recognition are designed to simulate the way the human brain analyzes and processes information. They are used to recognize complex patterns and make intelligent decisions based on the data.
8. What is 'clustering' in pattern recognition?
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Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups.
9. What is a 'support vector machine' (SVM) used for in pattern recognition?
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In pattern recognition, a Support Vector Machine (SVM) is a supervised learning model used for classification and regression analysis.
10. What is 'dimensionality reduction' in pattern recognition?
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Dimensionality reduction in pattern recognition involves reducing the number of input variables in a dataset, which can help to simplify models and avoid overfitting.
11. What does 'optical character recognition' (OCR) do?
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Optical character recognition is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo or from subtitle text superimposed on an image.
12. What is a 'decision tree' in pattern recognition?
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A decision tree in pattern recognition is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome.
13. What is 'Bayesian analysis' used for in pattern recognition?
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Bayesian analysis in pattern recognition involves using Bayesian techniques for classifying patterns, where the classification is based on the probability of events.
14. What is 'template matching' in pattern recognition?
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Template matching is a technique in digital image processing for finding parts of an image that match a template image. It is used in applications like object detection and recognition.
15. What is a 'feature vector' in pattern recognition?
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A feature vector in pattern recognition is an n-dimensional vector of numerical features that represent some object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics (called features) of an object in a mathematical, easily analyzable way.
16. What is 'principal component analysis' (PCA) used for in pattern recognition?
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Principal component analysis is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize by reducing the number of variables.
17. What is 'ensemble learning' in the context of pattern recognition?
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Ensemble learning in pattern recognition involves combining several machine learning models (like classifiers or experts) for improved prediction accuracy and robustness compared to a single model.
18. What does 'cross-validation' mean in pattern recognition?
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Cross-validation is a statistical method used in pattern recognition to assess the predictive performance of a model. It involves dividing a dataset into a training set and a validation set to test the model on unseen data.
19. What is 'k-nearest neighbors' (KNN) algorithm used for in pattern recognition?
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The k-nearest neighbors algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
20. What is a 'convolutional neural network' (CNN) primarily used for in pattern recognition?
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Convolutional Neural Networks are deep learning algorithms that are particularly powerful for analysis of images. They are used extensively in image and video recognition, recommender systems, and natural language processing.