Machine Learning
Machine learning is a subfield of computer science and mathematics that studies algorithms that improve their performance, or “learn”, or adapt, with experience and utilization of data. The algorithm “learn” patterns and features in the data that allow it to classify, cluster, or otherwise interpret the data. Machine learning, although distinct from artificial intelligence, has a connection to the latter field. In the context of the digital humanities, machine learning algorithms are adaptive algorithms that improve their performance through the data on which they act, in combination with specialized optimization (“learning”) approaches. Machine learning algorithms use sample data, called training data, as their input to generate a mathematical model for prediction, classification, or decision making. The resulting model is not programmed to make these predictions, classifications, or decisions. These capabilities emerge from the model based only on the training data.
The performance of machine learning algorithms depends on the availability of a large amount of training data. Consequently, machine learning has gained popularity because of the availability of large amounts of data through social media, the Web, sensors, and ubiquitous computing, thereby resulting in improved performance.
There are several classes of machine learning algorithms, or models. In supervised learning models, training data are labeled. The algorithm self-adapts to generate a model that maps the input training data to the output label. The goal is that the model generalizes. That is, the model will produce the correct output label given new, “unseen” input that was not included in the training data. Consequently, supervised learning is used for classification and prediction tasks.
Artificial neural networks (ANNs) are examples of supervised machine learning. ANNs are models inspired by the nexus of interconnected neurons in the human brain. One well-known application is recognition of handwritten characters. The ANN is presented with a large number of images of these characters, usually represented as a list of binary values from the pixels of an image.
For instance, an image of a character that has size 31 x 36 (31 rows and 36 columns) can be input into the network as a list of 1,116 binary values, indicating whether a pixel contains the character or background. The label for that image is the character the image represents. For example, and image of a handwritten “7” character would have the label 7. A large number of images of handwritten characters for each represented character and their associated labels are used as training data. If the ANN has been trained properly, then an image of a character not in the training data (for example, a “7” character written by a person whose handwriting was not used to train the network) would result in the correct label for that character, 7 in this case, as shown below. Each image is represented by 1,116 pixels,