Oded Maron Thesis

Oded Maron Thesis-72
The actual term multi-instance learning was introduced in the middle of the 1990s, by Dietterich et al.while they were investigating the problem of drug activity prediction.Their approach was to regard each molecule as a labeled bag, and all the alternative low-energy shapes of that molecule as instances in the bag, without individual labels. Solution to the multiple instance learning problem that Dietterich et al.

The actual term multi-instance learning was introduced in the middle of the 1990s, by Dietterich et al.while they were investigating the problem of drug activity prediction.Their approach was to regard each molecule as a labeled bag, and all the alternative low-energy shapes of that molecule as instances in the bag, without individual labels. Solution to the multiple instance learning problem that Dietterich et al.

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The bag is labeled positive ("beach") if it contains both "sand" region instances and "water" region instances.

Examples of where MIL is applied are: Numerous researchers have worked on adapting classical classification techniques, such as support vector machines or boosting, to work within the context of multiple-instance learning.

In machine learning, multiple-instance learning (MIL) is a type of supervised learning.

Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances.

They tried to create a learning systems that could predict whether new molecule was qualified to make some drug, or not, through analyzing a collection of known molecules.

Molecules can have many alternative low-energy states, but only one, or some of them, are qualified to make a drug.

If we can correctly identify this key, we can also correctly classify an entire key chain - positive if it contains the required key, or negative if it doesn't.

Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning.

If the space of instances is make the assumption regarding the relationship between the instances within a bag and the class label of the bag.

Because of its importance, that assumption is often called standard MI assumption.

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