1. Artificial Neural Networks
A neural network is a computational model composed of a large number of interconnected nodes (or neurons). Each node represents a specific output function, called the activation function. Each connection between two nodes represents a weighted value for the signal passing through that connection; this is equivalent to the memory of the artificial neural network. The network's output varies depending on the network's connection methods, weight values, and activation functions. The network itself is typically an approximation of a certain algorithm or function found in nature, or it may be an expression of a logical strategy.
Artificial neural networks (ANNs) are based on the brain's processing mechanisms and are used to develop algorithms for building complex patterns and making predictions. These algorithms perform exceptionally well in tasks such as speech, semantics, vision, and various games, but they require large amounts of data for training and demand high-performance hardware.
ANNs play a crucial role in image and character recognition, with handwritten character recognition finding numerous applications in fraud detection and even national security assessments. Research on ANNs paved the way for deep neural networks and forms the foundation of "deep learning," leading to a series of exciting innovations in computer vision, speech recognition, and natural language processing.
2. Decision Tree
In machine learning, a decision tree is a predictive model that represents a mapping between object attributes and object values. It uses a tree structure where each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category.
Decision tree algorithms are nonparametric and relatively easy to interpret, but they tend to overfit; they may get stuck in local minima; and they cannot learn online. The generation of a decision tree mainly consists of two steps: 1. Node splitting: When the attribute represented by a node cannot be determined, the node is split into two child nodes. 2. Threshold determination: An appropriate threshold is selected to minimize the classification error rate.
Classification trees (decision trees) are a commonly used classification method. It is a form of supervised learning. Supervised learning involves learning from a set of samples, each with a set of attributes and a pre-defined category, to create a classifier that can correctly classify newly encountered objects. This type of machine learning is called supervised learning.
3. Ensemble Algorithm
Simple algorithms are generally low in complexity, fast, and easy to demonstrate results. The models within them can be trained independently, and their predictions can be combined in some way to make an overall prediction. Each algorithm is like a specialist, and ensemble is the organization of simple algorithms, that is, multiple experts jointly determining the result.
Ensemble algorithms are much more accurate than predictions made using a single model, but require a lot of maintenance work.
AdaBoost's implementation is an incremental process, starting with a basic classifier and then finding the classifier that best handles the current misclassified sample at each step. The advantage is that it includes built-in feature selection, using only the effective features found in the training set. This reduces the number of features that need to be computed during classification and also addresses the problem of high-dimensional data being difficult to understand to some extent.
4. Regression Algorithm
Regression analysis, based on a series of known correlations between independent and dependent variables, establishes regression equations between these variables. These regression equations serve as algorithmic models to deduce the relationship between the dependent variable and new independent variables. Therefore, regression analysis is a practical predictive or classification model.
In linear regression, data is modeled using a linear prediction function, and unknown model parameters are estimated from the data. These models are called linear models. The most common linear regression modeling assumes that the conditional mean of y, given values of X, is an affine function of X. Less commonly, a linear regression model can be represented as a linear function of X, using the median or some other quantile of the conditional distribution of y given X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given values of X, rather than the joint probability distribution of X and y (the domain of multivariate analysis).
Linear regression was the first type of regression analysis to be rigorously studied and widely used in practice. This is because models that are linearly dependent on their unknown parameters are easier to fit than models that are nonlinearly dependent on their unknown parameters, and the statistical properties of the resulting estimates are also easier to determine.
Linear regression models are often fitted using least squares approximation, but they can also be fitted using other methods, such as minimizing the "fitting defect" in some other specifications (e.g., minimum absolute error regression), or minimizing the penalty of the least squares loss function in bridge regression. Conversely, least squares approximation can be used to fit nonlinear models. Therefore, although "least squares" and "linear models" are closely related, they are not equivalent.
5. Bayesian Algorithm
Naive Bayes classification is a very simple classification algorithm: given an item to be classified, calculate the probability of each category appearing given that the item appears, and the category with the highest probability is considered to be the category to which the item belongs.
Naive Bayes classification consists of three stages: 1. Determine the feature attributes based on the specific situation and appropriately divide each feature attribute to form a training sample set; 2. Calculate the frequency of occurrence of each category in the training samples and estimate the conditional probability of each feature attribute division for each category; 3. Use a classifier to classify the items to be classified.
Classification is a fundamental problem in data analysis and machine learning. Text classification has been widely applied in various fields such as web information filtering, information retrieval, and information recommendation. Data-driven classifier learning has been a hot topic in recent years, with many methods available, such as neural networks, decision trees, support vector machines, and Naive Bayes. Compared to other more sophisticated and elaborate classification algorithms, Naive Bayes is one of the classifiers with good learning efficiency and classification performance. It is an intuitive text classification algorithm and the simplest Bayesian classifier, with good interpretability. The key feature of Naive Bayes is the assumption that all features are independent and do not affect each other, and that each feature is equally important. However, this assumption does not hold true in the real world: firstly, there is a necessary connection between two adjacent words, meaning they cannot be independent; secondly, for an article, certain representative words determine its theme, without needing to read the entire article and examine all words. Therefore, appropriate feature selection methods are needed to achieve higher classification efficiency for the Naive Bayes classifier.
