# Pattern Recognition MCQs with Answers

Pattern Recognition MCQs are very important test and often asked by various testing services and competitive exams around the world. Here you will find all the Important Pattern Recognition MCQs for Preparation.

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## Pattern Recognition Online MCQs with Answers

Which of the following is a supervised learning algorithm used for pattern recognition?
a) K-means clustering
b) Decision tree
c) Apriori algorithm
d) Singular value decomposition

b) Decision tree

In pattern recognition, what is the purpose of feature extraction?
a) To reduce the dimensionality of the data
b) To remove noise from the data
c) To classify the data into categories
d) To evaluate the performance of the algorithm

a) To reduce the dimensionality of the data

Which of the following is a distance-based similarity measure used in pattern recognition?
a) Pearson correlation coefficient
b) Euclidean distance
c) Mutual information
d) Support vector machine

b) Euclidean distance

What is the purpose of cross-validation in pattern recognition?
a) To evaluate the performance of the algorithm
b) To preprocess the data
c) To visualize the data
d) To generate new features

a) To evaluate the performance of the algorithm

Which of the following is a common unsupervised learning algorithm used for pattern recognition?
a) Random forest
b) K-nearest neighbors
c) K-means clustering
d) Naive Bayes classifier

c) K-means clustering

What is the main objective of the k-nearest neighbors (k-NN) algorithm in pattern recognition?
a) To minimize the classification error
b) To maximize the feature space
c) To determine the optimal threshold
d) To perform dimensionality reduction

a) To minimize the classification error

Which of the following is a disadvantage of the k-means clustering algorithm?
a) It is sensitive to the initial centroid selection
b) It cannot handle high-dimensional data
c) It requires labeled training data
d) It is computationally expensive

a) It is sensitive to the initial centroid selection

What is the purpose of the confusion matrix in pattern recognition?
a) To measure the accuracy of the algorithm
b) To visualize the decision boundaries
c) To extract meaningful features from the data
d) To preprocess the data

a) To measure the accuracy of the algorithm

Which of the following is a feature selection technique used in pattern recognition?
a) Principal Component Analysis (PCA)
b) Support Vector Machine (SVM)
c) Genetic Algorithm (GA)
d) Naive Bayes classifier

c) Genetic Algorithm (GA)

What is the primary goal of the Naive Bayes classifier in pattern recognition?
a) To maximize the log-likelihood of the data
b) To minimize the sum of squared errors
c) To estimate the parameters of the data distribution
d) To compute the conditional probabilities of the classes

d) To compute the conditional probabilities of the classes

Which of the following is a technique for handling missing data in pattern recognition?
a) Mean imputation
b) Median imputation
c) Mode imputation
d) All of the above

d) All of the above

Which of the following is a linear classifier used in pattern recognition?
a) Decision tree
b) Random forest
c) Logistic regression

c) Logistic regression

What is the purpose of regularization in pattern recognition?
a) To prevent overfitting
b) To increase the complexity of the model
c) To reduce the computational cost
d) To handle missing data

a) To prevent overfitting

Which of the following is an ensemble learning technique used in pattern recognition?
a) Support Vector Machine (SVM)
b) K-nearest neighbors (k-NN)
c) Random forest
d) Decision tree

c) Random forest

What is the purpose of data normalization in pattern recognition?
a) To scale the features to a standard range
b) To remove outliers from the data
c) To compute the correlation between variables
d) To handle missing data

a) To scale the features to a standard range

Which of the following is a non-linear classifier used in pattern recognition?
a) Linear discriminant analysis (LDA)
b) Principal Component Analysis (PCA)
c) Support Vector Machine (SVM)
d) Naive Bayes classifier

c) Support Vector Machine (SVM)

What is the purpose of feature scaling in pattern recognition?
a) To transform the features to a new space
b) To normalize the features to a common scale
c) To select the most important features
d) To preprocess the data for visualization

b) To normalize the features to a common scale

Which of the following is a kernel function used in Support Vector Machines (SVM)?
a) Sigmoid function
b) ReLU function
c) Softmax function
d) Tanh function

a) Sigmoid function

What is the purpose of the receiver operating characteristic (ROC) curve in pattern recognition?
a) To visualize the decision boundaries of the classifier
b) To measure the performance of the classifier at different thresholds
c) To compute the feature importance scores
d) To preprocess the data for classification

b) To measure the performance of the classifier at different thresholds

Which of the following algorithms is based on the concept of a “support vector”?
a) Decision tree
b) K-means clustering
c) Support Vector Machine (SVM)
d) Random forest

c) Support Vector Machine (SVM)

What is the purpose of the Expectation-Maximization (EM) algorithm in pattern recognition?
a) To estimate the parameters of a probabilistic model
b) To compute the pairwise distances between samples
c) To preprocess the data for clustering
d) To visualize the data in a low-dimensional space

a) To estimate the parameters of a probabilistic model

Which of the following is a dimensionality reduction technique used in pattern recognition?
a) K-means clustering
b) Naive Bayes classifier
c) Principal Component Analysis (PCA)
d) Genetic Algorithm (GA)

c) Principal Component Analysis (PCA)

What is the purpose of feature engineering in pattern recognition?
a) To design and construct new features from the existing data
b) To preprocess the data for visualization
c) To compute the similarity between samples
d) To evaluate the performance of the classifier

a) To design and construct new features from the existing data

Which of the following is a generative model used in pattern recognition?
a) K-nearest neighbors (k-NN)
b) Gaussian Mixture Model (GMM)
c) Random forest
d) Decision tree

b) Gaussian Mixture Model (GMM)

What is the purpose of the training set in pattern recognition?
a) To learn the parameters of the model
b) To evaluate the performance of the model
c) To preprocess the data for visualization
d) To generate new features

a) To learn the parameters of the model

Which of the following is a method for handling class imbalance in pattern recognition?
a) Oversampling the minority class
b) Undersampling the majority class
c) SMOTE (Synthetic Minority Over-sampling Technique)
d) All of the above

d) All of the above

What is the purpose of the activation function in a neural network used for pattern recognition?
a) To introduce non-linearity into the model
b) To compute the weights of the connections
c) To determine the optimal threshold
d) To preprocess the data for classification

a) To introduce non-linearity into the model

Which of the following is a clustering algorithm used in pattern recognition?
a) Logistic regression
b) Support Vector Machine (SVM)
c) K-means clustering
d) Decision tree

c) K-means clustering

What is the purpose of the inertia criterion in K-means clustering?
a) To measure the separation between clusters
b) To compute the silhouette coefficients
c) To evaluate the performance of the classifier
d) To visualize the decision boundaries

a) To measure the separation between clusters

Which of the following is a technique for handling outliers in pattern recognition?
a) Winsorization
b) Z-score normalization
c) Min-max scaling
d) All of the above

d) All of the above

What is the purpose of the backpropagation algorithm in neural networks used for pattern recognition?
a) To update the weights of the connections based on the error
b) To compute the gradient of the activation function
c) To preprocess the data for visualization
d) To handle missing data

a) To update the weights of the connections based on the error

Which of the following is a distance metric used in K-nearest neighbors (k-NN)?
a) Manhattan distance
b) Chebyshev distance
c) Mahalanobis distance
d) All of the above

d) All of the above

What is the purpose of the Bag-of-Words model in text classification?
a) To preprocess the text data for visualization
b) To remove stop words from the text
c) To represent the text as a set of features
d) To compute the term frequency-inverse document frequency (TF-IDF)

c) To represent the text as a set of features

Which of the following is a supervised learning algorithm used for text classification?
a) K-means clustering
b) Decision tree
c) Naive Bayes classifier
d) Singular value decomposition

c) Naive Bayes classifier

What is the purpose of the n-gram model in natural language processing?
a) To compute the cosine similarity between documents
b) To perform sentiment analysis on text data
c) To generate new text samples
d) To capture the sequential information in text

d) To capture the sequential information in text

Which of the following is a technique for feature extraction from images in pattern recognition?
a) Principal Component Analysis (PCA)
b) Apriori algorithm
c) Support Vector Machine (SVM)
d) Decision tree

a) Principal Component Analysis (PCA)

What is the purpose of the Hough transform in image processing?
a) To detect edges in an image
b) To segment objects in an image
c) To recognize shapes in an image
d) To compress the image data

c) To recognize shapes in an image

Which of the following is a technique for object detection in computer vision?
a) Convolutional Neural Network (CNN)
b) Support Vector Machine (SVM)
c) Random forest
d) K-means clustering

a) Convolutional Neural Network (CNN)

What is the purpose of the Laplacian of Gaussian (LoG) filter in image processing?
a) To enhance the edges in an image
b) To denoise the image
c) To resize the image
d) To rotate the image

a) To enhance the edges in an image

Which of the following is a technique for image segmentation in computer vision?
a) K-means clustering
b) Principal Component Analysis (PCA)
c) Naive Bayes classifier
d) Genetic Algorithm (GA)

a) K-means clustering

What is the purpose of the mean-shift algorithm in image processing?
a) To detect the motion of objects in a video
b) To recognize faces in an image
c) To denoise the image
d) To compute the histogram of an image

a) To detect the motion of objects in a video

Which of the following is a technique for optical character recognition (OCR)?
a) K-nearest neighbors (k-NN)
b) Decision tree
c) Support Vector Machine (SVM)
d) Recurrent Neural Network (RNN)

d) Recurrent Neural Network (RNN)

What is the purpose of the scale-invariant feature transform (SIFT) in computer vision?
a) To recognize objects in images despite changes in scale and rotation
b) To segment the foreground and background in an image
c) To remove noise from the image
d) To visualize the features in the image

a) To recognize objects in images despite changes in scale and rotation

Which of the following is a technique for facial recognition in computer vision?
a) Decision tree
b) Principal Component Analysis (PCA)
c) Naive Bayes classifier
d) Fisher’s Linear Discriminant Analysis (FLDA)

d) Fisher’s Linear Discriminant Analysis (FLDA)

What is the purpose of the Radon transform in medical image analysis?
a) To detect tumors in medical images
b) To reconstruct the three-dimensional structure from two-dimensional images
c) To segment organs in medical images
d) To denoise the medical images

b) To reconstruct the three-dimensional structure from two-dimensional images

Which of the following is a technique for analyzing time series data?
a) Hidden Markov Model (HMM)
b) Decision tree
c) K-means clustering
d) Support Vector Machine (SVM)

a) Hidden Markov Model (HMM)

What is the purpose of the autocorrelation function in time series analysis?
a) To detect trends in the data
b) To measure the similarity between two time series
c) To compute the moving average of the data
d) To visualize the data in a low-dimensional space

b) To measure the similarity between two time series

Which of the following is a technique for anomaly detection in time series data?
a) Principal Component Analysis (PCA)
b) Apriori algorithm
c) Isolation Forest
d) Genetic Algorithm (GA)