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80 Result(s)
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Chapter
Dimensionality Reduction and Metric Learning
k-Nearest Neighbor (kNN) a commonly used supervised learning method with a simple mechanism: given a testing sample, find the k nearest training samples based on some distance metric, and then use these k ‘‘neig...
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Chapter
Computational Learning Theory
As the name suggests, computational learning theory is about ‘‘learning”’ by ‘‘computation” and is the theoretical foundation of machine learning. It aims to analyze the difficulties of learning problems, provide...
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Chapter
Probabilistic Graphical Models
The most important problem in machine learning is to estimate and infer the value of unknown variables (e.g., class label) based on the observed evidence (e.g., training samples). provide a framework that co...
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Chapter
Model Selection and Evaluation
In general, the proportion of incorrectly classified samples to the total number of samples is called error rate, that is, if a out of m samples are misclassified, then the error rate is \(E=a/m\) E = a / m
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Chapter
Reinforcement Learning
Planting watermelon involves many steps, such as seed selection, regular watering, fertilization, weeding, and insect control. We usually do not know the quality of the watermelons until harvesting. If we cons...
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Chapter
Decision Trees
Decision trees are a popular class of machine learning methods. Taking binary classification as an example, we can regard the task as deciding the answer to the question Is this instance positive? As the name ...
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Chapter
Support Vector Machine
Given a training set \(D = \{(\boldsymbol{x}_1, y_1), (\boldsymbol{x}_2, y_2), \ldots , (\boldsymbol{x}_m, y_m)\}\) D = { ( x 1 , y 1 ) , ( x 2 , y 2 ) , … , ( x m , y m ) } , wher...
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Chapter
Ensemble Learning
Ensemble learning, also known as multiple classifier system and committee-based learning, trains and combines multiple learners to solve a learning problem.
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Chapter
Feature Selection and Sparse Learning
Watermelons can be described by many attributes, such as color, root, sound, texture, and surface, but experienced people can determine the ripeness with only the root and sound information. In other words, not a...
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Chapter
Semi-Supervised Learning
We come to the watermelon field during the harvest season, and the ground is covered with many watermelons. The melon farmer brings a handful of melons and says that they are all ripe melons, and then points a...
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Chapter
Introduction
Following a drizzling, we take a walk on the wet street. Feeling the gentle breeze and seeing the sunset glow, we bet the weather must be nice tomorrow. Walking to a fruit stand, we pick up a green watermelon ...
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Chapter
Rule Learning
In machine learning, rules usually refer to logic rules in the form of ‘‘if \(\ldots ,\) … , then \(\ldots \) … ” that can describe regular patterns or domain concepts with clear semantics (Fürnkra...
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Chapter
Linear Models
Let \(\boldsymbol{x} = (x_1;x_2;\ldots ;x_d)\) x = ...
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Chapter
Neural Networks
Research neural networks started quite a long time ago, and it has become a broad and interdisciplinary research field today. Though neural networks have various definitions across disciplines, this book use...
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Chapter
Bayes Classifiers
Bayesian decision theory is a fundamental decision-making approach under the probability framework. In an ideal situation when all relevant probabilities were known, Bayesian decision theory makes optimal clas...
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Chapter
Clustering
Unsupervised learning aims to discover underlying properties and patterns from unlabeled training samples and lays the foundation for further data analysis. Among various unsupervised learning techniques, the mos...
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Book
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Chapter and Conference Paper
Wavelet-Based Emotion Recognition Using Single Channel EEG Device
Using computer technology to recognize emotion is the key to realize high-level human-computer interaction. Compared with facial and behavioral, physiological data such as EEG can detect real emotions more eff...
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Chapter and Conference Paper
An Adaptive Seed Node Mining Algorithm Based on Graph Clustering to Maximize the Influence of Social Networks
Recently, the issue of maximizing the influence of social networks is a hot topic. In large-scale social networks, the mining algorithm for maximizing influence seed nodes has made great progress, but only usi...
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Chapter and Conference Paper
Dense Subgraphs Summarization: An Efficient Way to Summarize Large Scale Graphs by Super Nodes
For large scale graphs, the graph summarization technique is essential, which can reduce the complexity for large-scale graphs analysis. The traditional graph summarization methods focus on reducing the comple...
