Machine Learning Algorithms

What are machine learning algorithms?

Before we can define machine learning algorithms, we must first provide an introduction to machine learning. Basically, machine learning is a computer’s ability to learn and solve problems without someone explicitly programming it. Machine learning studies the algorithms and mathematical models that computer systems use to improve, step by step, their performance of a particular task. It’s based on the notion that systems can learn from data and information, find patterns and autonomously make decisions with little human intervention.

Machine learning algorithms are the processes and rules a computer follows for solving a specific problem. These algorithms receive and analyse data to predict outcomes within a satisfactory range. As the algorithms receive additional data, they become ‘smarter’ over time, learning and optimising their actions to improve performance.

Machine learning algorithms fall into four main categories:

  • supervised
  • semisupervised
  • unsupervised
  • reinforcement

Supervised learning

With supervised learning, the computer learns by example. A human feeds the machine learning algorithm a known dataset that includes desired inputs and outputs, and the algorithm must figure out a way to arrive at those inputs and outputs. The algorithm finds patterns in the data, learns from observations and makes predictions, with the human correcting the computer along the way. This continues until the algorithm achieves a high degree of accuracy.

Semisupervised learning

Semisupervised learning employs both labelled and unlabelled data. Labelled data is basically information that has been tagged so the algorithm can understand it, whereas unlabelled data doesn’t have such tags. By using both, the machine learning algorithms can learn to label unlabelled data.

Unsupervised learning

With unsupervised learning, the machine learning algorithm examines data to pinpoint patterns without the aid of a human. The computer determines connections and relationships by analysing the available data. The machine learning algorithm must autonomously interpret large chunks of data and deal with it accordingly. It attempts to give the data organisation and structure. As the algorithm evaluates more data, its decision-making capability progressively improves.

Reinforcement learning

With reinforcement learning, computers receive a specific set of rules – actions, parameters and end values. Using these rules, the machine learning algorithm explores various possibilities and options, assessing and keeping track of each outcome to figure out the best one. In other words, this learning process entails trial and error.

Nexis Data as a Service

You can optimise your processes with machine learning, and Nexis Data as a Service (DaaS) can help.

Nexis DaaS compiles the data you need for predictive analytics so you can pinpoint your organisation’s reputational, financial, regulatory and strategic risks. This data includes:

  • an unrivalled collection of licensed global news data, with an archive dating back more than 40 years
  • a wide array of news, blogs and social media posts culled from the web
  • business and industry information
  • sanctions lists, watchlists and lists of politically exposed persons (PEPs)
  • legal data, such as court dockets and patents records

By taking advantage of Nexis DaaS’ reliable and relevant data, you can keep up your due diligence efforts and continuously monitor key customers, business partners, vendors, suppliers and other third parties.

With Nexis DaaS, you can also conduct statistical modelling and trends analysis. Complement your existing data pool with trusted, global content – identified and indexed for risk mitigation – into your own due diligence and monitoring workflows. For example, you can:

  • make faster, more informed decisions during customer onboarding and maintain a comprehensive view of risk potential for existing customers with the help of a risk application programming interface (API) and greater detail on people and organisations
  • identify changes on regulatory lists and negative news using keywords related to customers, suppliers and other third parties. Nexis DaaS integrates licensed news, sanctions lists, PEP lists and company profiles into your proprietary due-diligence system to understand, mitigate and manage risk
  • gain a broader scope of relevant data for your data science applications, so you can uncover trends that may signal disruptive events or potential opportunities.

Nexis DaaS comes in a range of APIs to suit your organisation’s specific content needs, technical capabilities and risk-mitigation workflow. With these APIs, you can search and retrieve data from LexisNexis servers using your proprietary, in-house business applications or a LexisNexis-approved third-party software solution. Alternatively, you can host bulk downloaded content on your own servers for use in data mining, machine learning and artificial intelligence applications.

Learn more about how Nexis DaaS can help you make more confident, data-driven decisions.