# Vector Record

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A Vector Record is a tuple data record (with a vector data structure) that represents a vector space point.

**Context:**- It can range from being a Dense Vector Record to being a Sparse Vector Record.
- It can range from being an Integer Vector Record to being a Rational Vector Record.
- It can be a Language Specific Vector Record.

**Example(s):**- a Scala Vector, Python Vector, Perl Vector, R Vector, ...
- a Bag-of-Words Record.
- a Vector-based Referencer, such as a vectorized learning record.
- a User Item-Ratings Vector Record.
- …

**Counter-Example(s):**- an Abstract Vector.
- an Array Data Object, such as:
`@Matrix1a = ([3, 1, 4], [2, 5, 11], [1, 7, 4], )`

**See:**Tuple Data, Matrix Record.

## References

### 2005

- (Bekkerman & McCallum, 2005) ⇒ Ron Bekkerman, and Andrew McCallum. (2005). “Disambiguating Web Appearance of People in a Social Network.” In: Proceedings of the 14th International World Wide Web Conference. (WWW 2005).
- QUOTE: Note that these all use average-link clustering methods: the distance between data points and cluster centroids is considered, not the distance between individual data instances.

### 2004

- (Bouchard & Triggs, 2004) ⇒ Guillaume Bouchard, and Bill Triggs. (2004). “The Trade-off Between Generative and Discriminative Classifiers.” In: Proceedings of COMPSTAT 2004.
- QUOTE: In supervised classification, inputs [math]\displaystyle{ x }[/math] and their labels [math]\displaystyle{ y }[/math] arise from an unknown joint probability
*p*(*x*,*y*). If we can approximate [math]\displaystyle{ p(x,y) }[/math] using a parametric family of models [math]\displaystyle{ G = {p_θ(x,y),θ ∈ Θ} }[/math], then a natural classifier is obtained by first estimating the class-conditional densities, then classifying each new data point to the class with highest posterior probability. This approach is called*generative*classification.

- QUOTE: In supervised classification, inputs [math]\displaystyle{ x }[/math] and their labels [math]\displaystyle{ y }[/math] arise from an unknown joint probability