Blog Archives

Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

June 6, 2016
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Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

Someone decided what data ought to go into the matrix. They placed the objects of interest in the rows and the features that differentiate among those objects into the columns. Decisions were made either to collect information or to store what was gath...

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Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

June 6, 2016
By
Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

Someone decided what data ought to go into the matrix. They placed the objects of interest in the rows and the features that differentiate among those objects into the columns. Decisions were made either to collect information or to store what was gath...

Read more »

Using Support Vector Machines as Flower Finders: Name that Iris!

May 25, 2016
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Using Support Vector Machines as Flower Finders: Name that Iris!

Nature field guides are filled with pictures of plants and animals that teach us what to look for and how to name what we see. For example, a flower finder might display pictures of different iris species, such as the illustrations in the plot below. Y...

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Using Support Vector Machines as Flower Finders: Name that Iris!

May 25, 2016
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Using Support Vector Machines as Flower Finders: Name that Iris!

Nature field guides are filled with pictures of plants and animals that teach us what to look for and how to name what we see. For example, a flower finder might display pictures of different iris species, such as the illustrations in the plot below. Y...

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The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

May 24, 2016
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The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

The "kernel" is the seed or the essence at the heart or the core, and the kernel function measures distance from that center. In the following example from Wikipedia, the kernel is at the origin and the different curves illustrate alternative depiction...

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The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

May 24, 2016
By
The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

The "kernel" is the seed or the essence at the heart or the core, and the kernel function measures distance from that center. In the following example from Wikipedia, the kernel is at the origin and the different curves illustrate alternative depiction...

Read more »

The Mad Hatter Explains Support Vector Machines

May 9, 2016
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The Mad Hatter Explains Support Vector Machines

"Hatter?" asked Alice, "Why are support vector machines so hard to understand?" Suddenly, before you can ask yourself why Alice is studying machine learning in the middle of the 19th century, the Hatter disappeared. "Where did he go?" thought Alice as ...

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The Mad Hatter Explains Support Vector Machines

May 9, 2016
By
The Mad Hatter Explains Support Vector Machines

"Hatter?" asked Alice, "Why are support vector machines so hard to understand?" Suddenly, before you can ask yourself why Alice is studying machine learning in the middle of the 19th century, the Hatter disappeared. "Where did he go?" thought Alice as ...

Read more »

When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

April 3, 2016
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When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

What is the value of a product feature? Within a market-based paradigm, the answer is the difference between revenues with and without the feature. A product can be decomposed into its features, each feature can be assigned a monetary value by includin...

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Choice Modeling with Features Defined by Consumers and Not Researchers

March 26, 2016
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Choice Modeling with Features Defined by Consumers and Not Researchers

Choice modeling begins with a researcher "deciding on what attributes or levels fully describe the good or service." This is consistent with the early neural networks in which features were precoded outside of the learning model. That is, choice modeli...

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