Below are three datasets for a subsset of text classification, sequential short text classification. All three datasets are for speech act prediction. Switchboard Dialog Act Corpus. [Jurafsky et al.1997] MRDA: ICSI Meeting Recorder Dialog Act Corpus (Janin et al., 2003; Shriberg et al., 2004) Dialog State Tracking Challenge 4's data set.
This is a dataset of chart images created from real data sources using matplotlib. There are 4 basic types of charts: Bar, Line, Scatter, Box. There are several tasks associated with this dataset including: 1) Chart Classification. 2) Text Detection and Recognition. 3) Text Role Classification. 4) Axis Analysis. 5) Legend Analysis
data set. Reference (Sebastiani, 2002) offers a brief survey on text classification. That paper introduces various techniques for text classification with a focus on machine learning solutions. One of these techniques is SVM which its results expose it as a promising technique for text classification (Dumais, Platt, Heckerman, & Sahami,
This tutorial classifies movie reviews as positive or negative using the text of the review. This is an example of binary — or two-class — classification, an important and widely applicable kind of machine learning problem. We’ll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These ...
LIBSVM Data: Classification (Binary Class) This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format. Many are from UCI, Statlog, StatLib and other collections. We thank their efforts. For most sets, we linearly scale each attribute to [-1,1] or [0,1].
This tutorial explains the basics of TensorFlow 2.0 with image classification as the example. 1) Data pipeline with dataset API. 2) Train, evaluation, save and restore models with Keras. 3) Multiple-GPU with distributed strategy. 4) Customized training with callbacks
Jul 15, 2019 · Video Classification with Keras and Deep Learning. 2020-06-12 Update: This blog post is now TensorFlow 2+ compatible! Videos can be understood as a series of individual images; and therefore, many deep learning practitioners would be quick to treat video classification as performing image classification a total of N times, where N is the total number of frames in a video.