Learningselect is a Christian learning organization that delivers theological learning experiences that are both affordable and effective. Its theological courses combine high-quality DVD-based video courses with proven textbooks and online learning. It is a member of the Lay School for Ministry Network of the ELCA. Its educational materials are designed to help people in their ministry practice the Christian faith.
The Lumina Select program is based on personality assessment. It helps to identify patterns of behavior that could contribute to a person’s success or hinder them from achieving their potential. The company’s research has shown that personality can account for 18% of the variance in job performance. As a result, the program helps recruiters identify individuals with high potential and high-quality performance.
Lumina Select offers a range of digital tools to support the selection process, including an integrated job profiler. This allows users to focus on the competencies that matter most to their specific role. The digital platform also allows users to create as many different job profiles as they need.
Learningselect’s flagship personality test, the Lumina Spark, gives users a detailed and personalized portrait of their strengths and development areas. The Learningselect.com results can be used to improve communication skills in the workplace, at home, or even in leadership development. The test measures 72 different personality traits and provides useful information about how to better meet your personal goals.
The Lumina Team module helps leaders and teams understand each other’s differences and similarities. This approach can help leaders develop more resilient and versatile leaders who inspire others to be their best. Lumina Team also helps teams work together more effectively, using impactful visuals and easy-to-grasp language. The tools help team members appreciate each other’s differences and complement each other, fostering respect and cooperation.
Exponentiated Gradient Exploration for Active Learning
Active learning algorithms have access to unlabeled examples. The algorithms can query an oracle for a label for each instance, but this is assumed to be expensive. Hence, the algorithms try to use as few labelled examples as possible.
Exponentiated Gradient Exploration (EG) is a type of optimization technique that improves active learning algorithms. It reduces the output variance and is applicable to any active learning algorithm.
Stream-Based Selective Sampling
Stream-Based Selective Sampling is a data mining technique that sends continuous samples of unlabeled data to a predictive model. This method avoids making assumptions about the distribution of data. It begins with a large unlabeled pool of data and evaluates the entire set before selecting a query.
Multi-instance learning is a branch of machine learning that uses collections of instances to train a classifier. Each instance is labeled with a set of covariates that describe its characteristics. The labeled instances contribute to the observed bag-level response. The problem is that instance labels are not always readily visible, so the goal of MIL is to predict bag labels based on instance-level covariates. This approach has been used for a wide variety of applications, from image concept learning to text categorization to cancer detection.
The approach is based on the principle of nearest-neighbor learning, but has been adapted for multi-instance data. In this approach, the nearest concept point in the feature space is located closest to positive instances, while farthest instances are farthest away. The optimal concept point is determined by the diversity density of the data points, which is a measure of the number of positive instances near the point and distance from negative instances.