Programs for Machine Learning. Part I

By Aiko M. Hormann. 1962

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “Random: it selects a subset of features randomly and, by using the selected features, generating as many binary splitting criteria as possible depending on the type of the feature [1], [2], [107].”
A SAT-Based Approach to Learn Explainable Decision Sets (2018, background): “Many other representations have been studied, including decision trees [34], rule lists [2], and sums of terms (i.”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “5 [281] constructs decision trees from data, selecting features and partitioning the dataset in the process.”
Logistic Model Trees (2003, background): “5 [12] and logistic regression on 32 UCI datasets [1], looking at classification accuracy and size of the constructed trees.”
Weed detection in soybean crops using ConvNets (2017, methodology): “5 is an algorithm that uses decision tree to perform the classification of a set of test cases (Quinlan, 1993).”
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging (2017, unknown): “The feature selection and classification training and testing were done using Weka software (Weka 3.8, University of Waikato).”
A meta-learning approach to automatic kernel selection for support vector machines (2006, background): “After fixing the best rule, the branch nodes send the final class value in the last node called the ‘leaf node’ [21,7].”
Contrast Pattern-Based Classification for Bot Detection on Twitter (2019, methodology): “In these tables, we also show each tested combination based on patterns such as a method for mining contrast patterns (LCMine [49] or RFm [48]), a measure for evaluating a split criteria on a decision tree (Hellinger distance [57], Bhattacharyya [58], and information gain [59]), if it was filtered (”
Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor (2007, methodology): “5 DT [9] (pruned) and the NB classifier using one Gaussian distribution per feature per class.”
Natural Language Generation (2004, methodology): “A recent study using the Charniak parser [11] as a lexicalized syntax model for generation purposes demonstrated the usefulness of these models in a variety of NLG tasks [12].”
Integrated visual vocabulary in latent Dirichlet allocation–based scene classification for IKONOS image (2014, unknown): “ε 1⁄4 1 100 1þM þ ðM þ 1ÞM 2 logðNMÞ; (20)”
Optimal trees for prediction and prescription (2018, background): “5 [104] and ID3 [103], is its fundamentally greedy nature.”
Advances in Intelligent Data Analysis V (2003, background): “If we prune in node 4 we lose [9,10], [9,12] and [9,14], and are left with [11,12], [11,14] and [13,14], i.”
Attentive Object Detection Using an Information Theoretic Saliency Measure (2004, methodology): “The induced decision tree is pruned using pessimistic error estimation [11].”