K-nearest Neighbor (KNN) classification algorithm is a relatively mature method in theory, and also one of the simplest machine learning algorithms. The idea of this method is: if a sample belongs to a certain category among the k most similar (that is, the closest neighbor) samples in the feature space, then the sample also belongs to this category. 1 definition if a sample in...

LSB is known as the Least Significant Bit, which is a simple and effective data hiding technique. The basic method of LSB steganography is to replace the lowest bit of the carrier image with the secret information to be embedded, and the high plane of the original image and the lowest plane representing the secret information constitute a new image containing hidden information. Graying the image for single channel...

BP neural network is a kind of multi-layer feedforward neural network. The commonly used three-layer structure is input layer - single hidden layer - output layer, as shown in the figure below. The main idea of BP neural network training: the input signal characteristic data is first mapped to the hidden layer (excitation)

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1. (1) Reinforcement learning is an area of machine learning that emphasizes how to act based on the environment in order to maximize expected benefits. Its inspiration comes from the theory of behaviorism in psychology, which is how an organism, stimulated by rewards or punishments given by the environment, gradually forms the expectation of the stimulus and produces the habitual behavior that can obtain the maximum benefit. In 1956, Bellman proposed the dynamic programming method. 1977...

Our goal is to find a hyperplane so that points closer to the hyperplane have greater spacing. So instead of thinking that all the points have to be away from the hyperplane, what we care about is finding a hyperplane that has the maximum distance between the points closest to it. For example, it is assumed that there are 5 samples for the blue star class and this type of samples is marked as Y =1, and 5 samples for the purple circle class and this type of samples is marked as Y =-1...

1) The original dual problem is also solved, but the QP problem in SVM (simplified solving process) is replaced by solving a linear equation set (caused by linear constraints in the optimization objective), which is also applicable to classification and regression tasks in high-dimensional input space; 2) In essence, it is the process of solving linear matrix equations, and the Gaussian processes...

1. When we compare two images, the first basic question we face is: when are two images the same or relatively similar, and how to measure the degree of similarity between these two images? Of course, the general method is that when the gray values of all the pixels of two images are the same, we consider the two images to be the same. This comparison method is feasible in certain application areas, such as constant light environment...

In order to solve the problem of local optimal solution, Kirkpatrick et al proposed simulated annealing algorithm (SA) in 1983, which can effectively solve the problem of local optimal solution. We know that in the world of molecules and atoms, the higher the energy, the less stable the molecules and atoms, and the lower the energy, the more stable the atoms. 'Annealing' is a physical term for the process of heating and cooling an object. Simulated annealing calculation...

Particle swarm optimization (PSO) is a numerical optimization algorithm based on swarm intelligence, which was proposed by social psychologist James Kennedy and electrical engineer Russell Eberhart in 1995. Since the birth of PSO, it has been improved in many ways. This section will introduce the basic principle and process of particle swarm optimization algorithm. 2. Particle Swarm optimization (PSO) is a population intelligence...