labeled data

Data Mining: Concepts and Techniques: Concepts and Techniques

by Jiawei Han, Micheline Kamber and Jian Pei  · 21 Jun 2011

. Cluster Analysis Unlike classification and regression, which analyze class-labeled (training) data sets, clustering analyzes data objects without consulting class labels. In many cases, class-labeled data may simply not exist at the beginning. Clustering can be used to generate class labels for a group of data. The objects are clustered or

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) and that the rule covers the tuple. A rule R can be assessed by its coverage and accuracy. Given a tuple, X, from a class-labeled data set, D, let be the number of tuples covered by R; be the number of tuples correctly classified by R; and be the number of

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on the accuracy measure can be deceiving when the main class of interest is in the minority. ■ Construction and evaluation of a classifier require partitioning labeled data into a training set and a test set. Holdout, random sampling, cross-validation, and bootstrapping are typical methods used for such partitioning. ■ Significance tests and

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distance, the more likely that errors will be corrected. 9.7.2. Semi-Supervised Classification Semi-supervised classification uses labeled data and unlabeled data to build a classifier. Let be the set of labeled data and be the set of unlabeled data. Here we describe a few examples of this approach for learning. Self

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-training is the simplest form of semi-supervised classification. It first builds a classifier using the labeled data. The classifier then tries to label the unlabeled data. The tuple with the most confident label prediction is added to the set of

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labeled data, and the process repeats (Figure 9.17). Although the method is easy to understand, a disadvantage is that it may reinforce errors. Figure 9.17

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data, Xu. Each classifier then teaches the other in that the tuple having the most confident prediction from f1 is added to the set of labeled data for f2 (along with its label). Similarly, the tuple having the most confident prediction from f2 is added to the set of

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labeled data for f1. The method is summarized in Figure 9.17. Cotraining is less sensitive to errors than self-training. A difficulty is that the assumptions

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learned from the training data under certain conditions. Clustering-based outlier detection methods have the following advantages. First, they can detect outliers without requiring any labeled data, that is, in an unsupervised way. They work for many data types. Clusters can be regarded as summaries of the data. Once the clusters are

Ajax: The Definitive Guide

by Anthony T. Holdener  · 25 Jan 2008  · 982pp  · 221,145 words

in the first place. Example 11-9 shows the JavaScript necessary to perform such an action. Example 11-9. Switching out the label data /* Example 11-9. Switching out the label data. */ /** * This function, reloadForm, takes the XMLHttpRequest JSON server response * /xhrResponse/ from the server and sets it equal to the global <label> element

Natural Language Annotation for Machine Learning

by James Pustejovsky and Amber Stubbs  · 14 Oct 2012  · 502pp  · 107,510 words

tries to find structure from an input set of unlabeled data. Semi-supervised learning Any technique that generates a function mapping from inputs of both labeled data and unlabeled data; a combination of both supervised and unsupervised learning. Table 1-4 shows a general overview of ML algorithms and some of the

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. For example, in order to distinguish spam from not-spam in your email inbox, an algorithm called a classifier is trained on a set of labeled data, where individual emails have been assigned the label [+spam] or [-spam]. It is the presence of certain (known) words or phrases in an email that

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later chapter, when we discuss the impact of the initial model on the subsequent performance of the algorithms you are trying to train over your labeled data. Warning In later chapters, we will see that there are actually several models that might be appropriate for describing a phenomenon, each providing a different

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tries to find structure from an input set of unlabeled data. Semi-supervised learning Any technique that generates a function mapping from inputs of both labeled data and unlabeled data; a combination of both supervised and unsupervised learning. Because our focus is on how annotated data can improve a system’s performance

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methods, and these are called, not surprisingly, probabilistic clustering approaches. Semi-Supervised Learning Semi-supervised learning (SSL) is a method of learning that employs both labeled data as well as unlabeled data. It is an attractive idea for language-based tasks in particular, because of two reasons: there is an enormous amount

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labeled training data can bring about a degradation in performance rather than an improvement. However, for many tasks, SSL can actually perform better than using labeled data alone (Singh et al. 2008), depending on the nature of the domain to be explored, as well as the type of labeling task involved. A

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may not be the ones that you have in mind for your task. Semi-supervised learning techniques allow you to use a small amount of labeled data to generate labels for larger sets of data, and are an effective way to deal with very large datasets. If you aren’t sure where

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

by Foster Provost and Tom Fawcett  · 30 Jun 2013  · 660pp  · 141,595 words

. The input data for the induction algorithm, used for inducing the model, are called the training data. As mentioned in Chapter 2, they are called labeled data because the value for the target variable (the label) is known. Let’s return to our example churn problem. Based on what we learned in

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training and 1/k used for testing. Figure 5-9. An illustration of cross-validation. The purpose of cross-validation is to use the original labeled data efficiently to estimate the performance of a modeling procedure. Here we show five-fold cross-validation: the original dataset is split randomly into five equal

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different based on different collections of evidence E—in our example, different sets of websites visited. As mentioned above, we would like to use some labeled data, such as the data from our randomly targeted campaign, to associate different collections of evidence E with different probabilities. Unfortunately, this introduces a key problem

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training data. Sometimes we can specify the target variable precisely, but we find we do not have any labeled data. In certain cases, we can use micro-outsourcing systems such as Mechanical Turk to label data. For example, advertisers would like to keep their advertisements off of objectionable web pages, like those that contain

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for attribute. Class (label) One of a small, mutually exclusive set of labels used as possible values for the target variable in a classification problem. Labeled data has one class label assigned to each example. For example, in a dollar bill classification problem the classes could be legitimate and counterfeit. In a

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Concepts of Data Science Kosinski, Michal, Example: Evidence Lifts from Facebook “Likes”–Example: Evidence Lifts from Facebook “Likes” L L2 norm (equation), * Other Distance Functions labeled data, Models, Induction, and Prediction labels, Supervised Versus Unsupervised Methods Ladyburn single malt scotch, Understanding the Results of Clustering Laphroaig single malt scotch, Understanding the Results

Mastering Pandas

by Femi Anthony  · 21 Jun 2015  · 589pp  · 69,193 words

.washington.edu/~pedrod/papers/cacm12.pdf Supervised versus unsupervised learning For supervised learning problems, the input to a learning problem is a dataset consisting of labeled data. By this we mean that we have outputs whose values are known. The learning program is fed input samples and their corresponding outputs and its

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goal is to decipher the relationship between them. Such input is known as labeled data. Supervised learning problems include the following: Classification: The learned attribute is categorical (nominal) or discrete Regression: The learned attribute is numeric/continuous In unsupervised learning

Code: The Hidden Language of Computer Hardware and Software

by Charles Petzold  · 28 Sep 1999  · 566pp  · 122,184 words

that indicate what the automated adder is supposed to do with the corresponding address in the original RAM array. These two RAM arrays can be labeled Data (the original RAM array) and Code (the new one): We've already established that our new automated adder needs to be able to write sums

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into the original RAM array (labeled Data). But the new RAM array (labeled Code) will be written to solely through the control panel. We need four codes for the four actions we

The Elements of Statistical Learning (Springer Series in Statistics)

by Trevor Hastie, Robert Tibshirani and Jerome Friedman  · 25 Aug 2009  · 764pp  · 261,694 words

as generalizations allowing for different variable types and more general distance measures, are given in Chapter 14. To use K-means clustering for classification of labeled data, the steps are: • apply K-means clustering to the training data in each class separately, using R prototypes per class; • assign a class label to

Data Science from Scratch: First Principles with Python

by Joel Grus  · 13 Apr 2015  · 579pp  · 76,657 words

to make a binary judgment. Is this email spam? Should we hire this candidate? Is this air traveler secretly a terrorist? Given a set of labeled data and such a predictive model, every data point lies in one of four categories: True positive: “This message is spam, and we correctly predicted spam

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this chapter, we’ll focus on classification trees, and we’ll work through the ID3 algorithm for learning a decision tree from a set of labeled data, which should help us understand how decision trees actually work. To make things simple, we’ll restrict ourselves to problems with binary outputs like “should

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are: def class_probabilities(labels): total_count = len(labels) return [count / total_count for count in Counter(labels).values()] def data_entropy(labeled_data): labels = [label for _, label in labeled_data] probabilities = class_probabilities(labels) return entropy(probabilities) The Entropy of a Partition What we’ve done so far is compute the entropy (think

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“uncertainty”) of a single set of labeled data. Now, each stage of a decision tree involves asking a question whose answer partitions data into one or (hopefully) more subsets. For instance, our “does

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we can implement as: def partition_entropy(subsets): """find the entropy from this partition of data into subsets subsets is a list of lists of labeled data""" total_count = sum(len(subset) for subset in subsets) return sum( data_entropy(subset) * len(subset) / total_count for subset in subsets ) Note One problem

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a prediction). We will build it using the relatively simple ID3 algorithm, which operates in the following manner. Let’s say we’re given some labeled data, and a list of attributes to consider branching on. If the data all have the same label, then create a leaf node that predicts that

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mad Robert Herrick Most of the algorithms in this book are what’s known as supervised learning, in that they start with a set of labeled data and use that as the basis for making predictions about new, unlabeled data. Clustering, however, is an example of unsupervised learning, in which we work

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

by Zdravko Markov and Daniel T. Larose  · 5 Apr 2007

. Using the mixture model, we can define three problems: a finite mixture problem, a classification problem, and a clustering problem. Finite Mixture Problem Given a labeled data set (i.e., we know the class for each attribute value) the problem is to find the mean, standard deviation, and the probability of sampling

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, 0, 0, 0, 0.976, 0.254)) = Clustering Problem So far we have discussed two tasks associated with our probabilistic setting: learning (creating models given labeled data) and classification (predicting labels using models). Recall, however, that the cluster labels were created automatically by k-means clustering. So a natural question is whether

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-clusters accuracies (or errors) of the splits based on their values. Note, however, that this is a supervised method for attribute evaluation because it uses labeled data. Interestingly, the two best attributes selected by error-based evaluation are the same as those preferred by the unsupervised MDL evaluation approach. This is an

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clustering: error-based and information gain attribute evaluation. In fact, these two schemes are used mostly in classification but can also work in clustering when labeled data are available. Hereafter we formally define information gain, describe a similarity-based approach, and illustrate the effect of feature selection with the performance of the

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–84 Bayes rule, 76 classification problem, 76–78 clustering problem, 78–84 expectation maximization (EM) algorithm, 79 finite mixture problem, 74–75 independence assumption, 77 labeled data set, 74 log-likelihood criterion function, 80 mean, class, 75 naive Bayes, 77 optimization, 79 probability density function, 76 probability of sampling, 75 standard deviation

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron  · 13 Mar 2017  · 1,331pp  · 163,200 words

to each other. Semisupervised learning Some algorithms can deal with partially labeled training data, usually a lot of unlabeled data and a little bit of labeled data. This is called semisupervised learning (Figure 1-11). Some photo-hosting services, such as Google Photos, are good examples of this. Once you upload all

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stacked autoencoder using all the data, then reuse the lower layers to create a neural network for your actual task, and train it using the labeled data. For example, Figure 15-8 shows how to use a stacked autoencoder to perform unsupervised pretraining for a classification neural network. The stacked autoencoder itself

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its lower half for the classifier (i.e., reuse the layers up to the codings layer, included) and train the classifier using the labeled data. If you have little labeled data, you probably want to freeze the reused layers when training the classifier. The fact that an autoencoder perfectly reconstructs its inputs does not

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