机器学习第二版中Tom准备增加的内同，关于生成模型、判别模型以及朴素贝叶斯和逻辑回归的介绍，在作者主页下载的

使用神经网络和其他分类器进行环境声音分类ESC 使用音频数据集进行音频特征提取和分类 包含ECS-10音频数据。 它包含10类不同的环境声音（海浪，孩子们玩耍等） 主要目标是比较6个经过测试的分类器的分类准确性。 依存关系 带有Python 2.7的Anaconda 2。 （尚未测试Python 3.6） Librosa（音频加载，音频可视化和特征提取） 科学工具学习 Keras（Theano后端） Numpy，Matplotlib 熊猫（数据可视化） Jupyter笔记本 添加了Jupyter Notebook（Python 2.7内核）以说明工作流程。 用于特征提取和分类的脚本已作为.py文件添加，并随后全部加载到Jupyter Notebook中。 运行feature_extraction.py label.npy创建一个numpy数组（ feature.npy ），

用于成本敏感分类的Adaboost扩展CSExtension 1 CSExtension 2 CSExtension 3 CSExtension 4 CSExtension 5 AdaCost Boost CostBoost Uboost CostUBoost AdaBoostM1实施集群中所有列出的“成本敏感分类”算法。 它们是需要基础算法（例如决策树）的元算法，此外，还需要投票标准，例如最低期望成本标准。输入还需要加载arff文件和成本矩阵（上载arff和成本文件样本供用户参考）扩展程序使用weka进行分类，并生成分类模型以及混淆矩阵。 对于给定的数据集和成本矩阵

L. Kuncheva 2004 年的 Combining Pattern Classifiers Methods and Algorithms

One of the most exciting recent developments in machine learning is the discovery and elaboration of kernel methods for classification and regression. These algorithms combine three important ideas into a very successful whole. From mathematical programming, they exploit quadratic programming algorithms for convex optimization; from mathematical analysis, they borrow the idea of kernel representations; and from machine learning theory, they adopt the objective of finding the maximum-margin classifier. After the initial development of support vector machines, there has been an explosion of kernel-based methods. Ralf Herbrich’s Learning Kernel Classifiers is an authoritative treatment of support vector machines and related kernel classification and regression methods. The book examines these methods both from an algorithmic perspective and from the point of view of learning theory. The book’s extensive appendices provide pseudo-code for all of the algorithms and proofs for all of the theoretical results. The outcome is a volume that will be a valuable classroom textbook as well as a reference for researchers in this exciting area. The goal of building systems that can adapt to their environment and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientific and industrial fields. Recently, several research communities have begun to converge on a common set of issues surrounding supervised, unsupervised, and reinforcement learning problems. TheMIT Press series on Adaptive Computation and Machine Learning seeks to unify the many diverse strands of machine learning research and to foster high quality research and innovative applications. Thomas Dietterich

audio_features_popularity_classifiers

Semantic segmentation based on fusion of features and classifiers

Machine rule induction was examined on a difficult categorization problem by applying a Hollandstyle classifier system to a complex letter recognition task. A set of 20,000 unique letter images was generated by randomly distorting pixel images of the 26 uppercase letters from 20 different commercial fonts. The parent fonts represented a full range of character types including script, italic, serif, and Gothic. The features of each of the 20,000 characters were summarized in terms of 16 primitive numerical attributes. Our research focused on machine induction techniques for generating IF-THEN classifiers in which the IF part was a list of values for each of the 16 attributes and the THEN part was the correct category, i.e., one of the 26 letters of the alphabet. We examined the effects of different procedures for encoding attributes, deriving new rules, and apportioning credit among the rules. Binary and Gray-code attribute encodings that required exact matches for rule activation were compared with integer representations that employed fuzzy matching for rule activation. Random and genetic methods for rule creation were compared with instance-based generalization. The strength/specificity method for credit apportionment was compared with a procedure we call "accuracy/utility."