zz Mac: Terminal 下不同类型的文件显示不同的颜色

原文: http://www.macfans.com.cn/thread-34827-1-1.html

Terminal 下不同类型的文件显示不同的颜色
Terminal 默认的 shell 是 bash (提示符是 $)
在 ~ 先建立一个文件  ~/.bash_profile 
加入下面的两行：
export CLICOLOR=1
export LSCOLORS=gxfxaxdxcxegedabagacad

存盘， 退出Terminal, 重起Terminal.
更改LSCOLORS可以有不同的颜色效果。 

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如果你用的是 csh/tcsh (提示符是 %), 建立  ~/.cshrc 加入
setenv CLICOLOR 1
setenv LSCOLORS gxfxaxdxcxegedabagacad

存盘， 执行 source ~/.cshrc
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LSCOLORS 的含义：

    LSCOLORS The value of this variable describes what color to use
    for which attribute when colors are enabled with
    CLICOLOR. This string is a concatenation of pairs of the
    format fb, where f is the foreground color and b is the
    background color.

    The color designators are as follows:

    a black
    b red
    c green
    d brown
    e blue
    f magenta
    g cyan
    h light grey
    A bold black, usually shows up as dark grey
    B bold red
    C bold green
    D bold brown, usually shows up as yellow
    E bold blue
    F bold magenta
    G bold cyan
    H bold light grey; looks like bright white
    x default foreground or background

    Note that the above are standard ANSI colors. The actual
    display may differ depending on the color capabilities of
    the terminal in use.

    The order of the attributes are as follows:

    1. directory
    2. symbolic link
    3. socket
    4. pipe
    5. executable
    6. block special
    7. character special
    8. executable with setuid bit set
    9. executable with setgid bit set
    10. directory writable to others, with sticky bit
    11. directory writable to others, without sticky
    bit

    The default is “exfxcxdxbxegedabagacad”, i.e. blue fore-
    ground and default background for regular directories,
    black foreground and red background for setuid executa-
    bles, etc.

One accepted paper by Neurocomputing is online now.

A general framework for co-training and its applications

Abstract

Co-training is one of the major semi-supervised learning paradigms in which two classifiers are alternately trained on two distinct views and they teach each other by adding the predictions of unlabeled data to the training set of the other view. Co-training can achieve promising performance, especially when there is only a small number of labeled data. Hence, co-training has received considerable attention, and many variant co-training algorithms have been developed. It is essential and informative to provide a systematic framework for a better understanding of the common properties and differences in these algorithms. In this paper, we propose a general framework for co-training according to the diverse learners constructed in co-training. Specifically, we provide three types of co-training implementations, including co-training on multiple views, co-training on multiple classifiers, and co-training on multiple manifolds. Finally, comprehensive experiments of different methods are conducted on the UCF-iPhone dataset for human action recognition and the USAA dataset for social activity recognition. The experimental results demonstrate the effectiveness of the proposed solutions.

Keywords

http://www.sciencedirect.com/science/article/pii/S0925231215006426

One accepted paper is online now.

Manifold regularized kernel logistic regression for web image annotation

Abstract

With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.

Keywords

Manifold regularization; Kernel logistic regression; Laplacian Eigenmaps; Semi-supervised learning; Image annotation

doi:10.1016/j.neucom.2014.06.096