In multi-instance learning, there are two kinds of prediction failure, i.e., false negative and false positive. Current research mainly focus on avoding the former. We attempt to utilize the geometric distribution of instances inside positive bags to avoid both the former and the latter. Based on kernel principal component analysis, we define a projection constraint for each positive bag to classify its constituent instances far away from the separating hyperplane while place positive instances and negative instances at opposite sides. We apply the Constrained Concave-Convex Procedure to solve the resulted problem.
To evaluate whether the strategy or approach you're evaluating requires artificial intelligence, let's turn back to our definition of AI as any computer-based system that observes, analyzes, and learns. Thus, a true AI system is able to sense its own environment and augment its base of knowledge in close to real time. A Tesla's onboard computers analyze the images, blips, and other data it collects to make sense of its surroundings, allowing for the automation of several driving decisions. Using this data, companies and sales professionals are able to arrive at many counterintuitive insights -- for instance, calls with more positive sentiment are actually associated with lower closing rates than calls with less positive sentiment. The ability to test, learn, and improve is only available to the most advanced machine learning systems today.
Calculating precision and recall is actually quite easy. Imagine there are 100 positive cases among 10,000 cases. You want to predict which ones are positive, and you pick 200 to have a better chance of catching many of the 100 positive cases. You record the IDs of your predictions, and when you get the actual results you sum up how many times you were right or wrong.
The learning of appropriate distance metrics is a critical problem in classification. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. BoostMetric is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices.
Imagine a machine learning algorithm is tasked with identifying the number of bananas within a bowl of fruit. In total, the bowl contains 10 pieces of fruit, 4 of which are bananas, and 6 are apples. The algorithm determines that there are 5 bananas, and 5 apples. The number of bananas that were counted correctly are known as true positives, while the items that were identified incorrectly as bananas are called false positives. In this example, there are 4 true positives, and one false positive, making the algorithms precision 4/5, and its recall is 4/10.