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简介： 机器学习是从数据和经验中学习的算法研究。 它被广泛应用于从医学到广告，从军事到行人的各种应用领域。 CIML是一组入门资料，涵盖了现代机器学习的大多数主要方面（监督学习，无监督学习，大幅度方法，概率建模，学习理论等）。 它的重点是具有严格主干的广泛应用。 一个子集可以用于本科课程； 研究生课程可能涵盖全部材料，然后再覆盖一些。
作者介绍： Hal Daumé III，教授，他曾担任Perotto教授职位，他现在Microsoft Research NYC的机器学习小组中。 研究方向是自然语言处理。
大纲介绍：
- 前言
- 决策树
- Limits of Learning
- 近邻算法
- 感知机
- 联系
- 边缘分类
- 线性模型
- 偏差
- 概率模型
- 神经网络
- 核函数
- 学习理论
- Ensemble 方法
- 高效学习
- 无监督学习
- 期望最大化
- 结构预测
- 模仿学习
- 后记
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Speech Emotion Recognition (SER) is becoming a key role in global business today to improve service efficiency, like call center services. Recent SERs were based on a deep learning approach. However, the efficiency of deep learning depends on the number of layers, i.e., the deeper layers, the higher efficiency. On the other hand, the deeper layers are causes of a vanishing gradient problem, a low learning rate, and high time-consuming. Therefore, this paper proposed a redesign of existing local feature learning block (LFLB). The new design is called a deep residual local feature learning block (DeepResLFLB). DeepResLFLB consists of three cascade blocks: LFLB, residual local feature learning block (ResLFLB), and multilayer perceptron (MLP). LFLB is built for learning local correlations along with extracting hierarchical correlations; DeepResLFLB can take advantage of repeatedly learning to explain more detail in deeper layers using residual learning for solving vanishing gradient and reducing overfitting; and MLP is adopted to find the relationship of learning and discover probability for predicted speech emotions and gender types. Based on two available published datasets: EMODB and RAVDESS, the proposed DeepResLFLB can significantly improve performance when evaluated by standard metrics: accuracy, precision, recall, and F1-score.
最新论文
Speech Emotion Recognition (SER) is becoming a key role in global business today to improve service efficiency, like call center services. Recent SERs were based on a deep learning approach. However, the efficiency of deep learning depends on the number of layers, i.e., the deeper layers, the higher efficiency. On the other hand, the deeper layers are causes of a vanishing gradient problem, a low learning rate, and high time-consuming. Therefore, this paper proposed a redesign of existing local feature learning block (LFLB). The new design is called a deep residual local feature learning block (DeepResLFLB). DeepResLFLB consists of three cascade blocks: LFLB, residual local feature learning block (ResLFLB), and multilayer perceptron (MLP). LFLB is built for learning local correlations along with extracting hierarchical correlations; DeepResLFLB can take advantage of repeatedly learning to explain more detail in deeper layers using residual learning for solving vanishing gradient and reducing overfitting; and MLP is adopted to find the relationship of learning and discover probability for predicted speech emotions and gender types. Based on two available published datasets: EMODB and RAVDESS, the proposed DeepResLFLB can significantly improve performance when evaluated by standard metrics: accuracy, precision, recall, and F1-score.
