感知机在机器学习中,感知机是一种二进制分类器监督学习的算法。二值分类器是一个函数,它可以决定输入是否属于某个特定的类,输入由一个数字向量表示。它是一种线性分类器,即基于线性预测函数结合一组权值和特征向量进行预测的分类算法。

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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

下载链接: https://pan.baidu.com/s/1QwSGTioJxDCRvlkBqcJr_A

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Surface normal estimation from a single image is an important task in 3D scene understanding. In this paper, we address two limitations shared by the existing methods: the inability to estimate the aleatoric uncertainty and lack of detail in the prediction. The proposed network estimates the per-pixel surface normal probability distribution. We introduce a new parameterization for the distribution, such that its negative log-likelihood is the angular loss with learned attenuation. The expected value of the angular error is then used as a measure of the aleatoric uncertainty. We also present a novel decoder framework where pixel-wise multi-layer perceptrons are trained on a subset of pixels sampled based on the estimated uncertainty. The proposed uncertainty-guided sampling prevents the bias in training towards large planar surfaces and improves the quality of prediction, especially near object boundaries and on small structures. Experimental results show that the proposed method outperforms the state-of-the-art in ScanNet and NYUv2, and that the estimated uncertainty correlates well with the prediction error. Code is available at https://github.com/baegwangbin/surface_normal_uncertainty.

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Surface normal estimation from a single image is an important task in 3D scene understanding. In this paper, we address two limitations shared by the existing methods: the inability to estimate the aleatoric uncertainty and lack of detail in the prediction. The proposed network estimates the per-pixel surface normal probability distribution. We introduce a new parameterization for the distribution, such that its negative log-likelihood is the angular loss with learned attenuation. The expected value of the angular error is then used as a measure of the aleatoric uncertainty. We also present a novel decoder framework where pixel-wise multi-layer perceptrons are trained on a subset of pixels sampled based on the estimated uncertainty. The proposed uncertainty-guided sampling prevents the bias in training towards large planar surfaces and improves the quality of prediction, especially near object boundaries and on small structures. Experimental results show that the proposed method outperforms the state-of-the-art in ScanNet and NYUv2, and that the estimated uncertainty correlates well with the prediction error. Code is available at https://github.com/baegwangbin/surface_normal_uncertainty.

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