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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Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using variational autoencoders (VAEs) and multi-layer perceptrons (MLPs) to predict anti-cancer drug responses. Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data and encodes these data with our {\sc {GeneVae}} model, which is an ordinary VAE model, and a rectified junction tree variational autoencoder ({\sc JTVae}) model, respectively. A multi-layer perceptron processes these encoded features to produce a final prediction. Our tests show our system attains a high average coefficient of determination ($R^{2} = 0.83$) in predicting drug responses for breast cancer cell lines and an average $R^{2} = 0.845$ for pan-cancer cell lines. Additionally, we show that our model can generates effective drug compounds not previously used for specific cancer cell lines.

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Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using variational autoencoders (VAEs) and multi-layer perceptrons (MLPs) to predict anti-cancer drug responses. Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data and encodes these data with our {\sc {GeneVae}} model, which is an ordinary VAE model, and a rectified junction tree variational autoencoder ({\sc JTVae}) model, respectively. A multi-layer perceptron processes these encoded features to produce a final prediction. Our tests show our system attains a high average coefficient of determination ($R^{2} = 0.83$) in predicting drug responses for breast cancer cell lines and an average $R^{2} = 0.845$ for pan-cancer cell lines. Additionally, we show that our model can generates effective drug compounds not previously used for specific cancer cell lines.

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