# US 10,504,038 B2 · Refined learning data representation for classifiers

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- Number: US 10,504,038 B2
- Document ID: US10504038B2
- Status: Granted
- Kind: B2
- Application number: 15/143,792
- Filed: 2016-05-02
- Published: 2017-11-02
- Granted: 2019-12-10
- Earliest claimed: 2016-05-02
- Assignees: Cisco Technology, Inc.
- Applicants: Cisco Technology, Inc.
- Inventors: Vojtech Franc; Karel Bartos; Michal Sofka
- Web page: https://patentgrep.com/patent/US10504038B2

## CPC

- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06F 17/11: for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06N 20/00: Machine learning
- H04L 63/1425: {Traffic logging, e.g. anomaly detection}
- G06F 21/552: {involving long-term monitoring or reporting}
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

In one embodiment, a learning machine device initializes thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins (e.g., uniform and equidistant bins). Next, adjacent bins of the pre-defined bins having substantially similar weights may be reciprocally merged, the merging resulting in a second number of refined bins that is less than the first number. Notably, while merging, the device also learns weights of a linear decision rule associated with the one or more data features. Accordingly, a data-driven representation for a data-driven classifier may be established based on the refined bins and learned weights.

## Claims

### Claim 1

A method, comprising:
initializing, at a learning machine device, thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins;
reciprocally merging adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number;
simultaneously learning weights of a linear decision rule associated with the one or more data features while merging; and
establishing a data-driven representation for a data-driven classifier based on the refined bins and learned weights.

### Claim 2 (depends on 1)

The method as in claim 1, wherein the pre-defined bins are uniform and equidistant.

### Claim 3 (depends on 1)

The method as in claim 1, further comprising:
using the data-driven classifier on traffic in a computer network.

### Claim 4 (depends on 1)

The method as in claim 1, further comprising:
sharing the data-driven classifier with one or more other devices.

### Claim 5 (depends on 1)

The method as in claim 1, wherein the data-driven classifier is a linear support vector machine (SVM).

### Claim 6 (depends on 1)

The method as in claim 1, wherein substantially similar weights comprise one or more of equal weights, similar weights, and weights having a same sign.

### Claim 7 (depends on 1)

The method as in claim 1, wherein the one or more features comprise a feature pair, and wherein correlation between feature values of the feature pair is used for the data representation.

### Claim 8 (depends on 1)

The method as in claim 1, wherein the one or more features are binarized.

### Claim 9 (depends on 1)

The method as in claim 1, wherein the one or more features are real-valued.

### Claim 10

An apparatus, comprising:
a processor configured to execute one or more processes; and
a memory configured to store a process executable by the processor, the process when executed operable to:
initialize thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins;
reciprocally merge adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number;
simultaneously learn weights of a linear decision rule associated with the one or more data features while merging; and
establish a data-driven representation for a data-driven classifier based on the refined bins and learned weights.

### Claim 11 (depends on 10)

The apparatus as in claim 10, wherein the pre-defined bins are uniform and equidistant.

### Claim 12 (depends on 10)

The apparatus as in claim 10, wherein the process when executed is further operable to:
use the data-driven classifier on traffic in a computer network.

### Claim 13 (depends on 10)

The apparatus as in claim 10, wherein the process when executed is further operable to:
share the data-driven classifier with one or more other devices.

### Claim 14 (depends on 10)

The apparatus as in claim 10, wherein the data-driven classifier is a linear support vector machine (SVM).

### Claim 15 (depends on 10)

The apparatus as in claim 10, wherein substantially similar weights comprise one or more of equal weights, similar weights, and weights having a same sign.

### Claim 16 (depends on 10)

The apparatus as in claim 10, wherein the one or more features comprise a feature pair, and wherein correlation between feature values of the feature pair is used for the data representation.

### Claim 17 (depends on 10)

The apparatus as in claim 10, wherein the one or more features are binarized.

### Claim 18 (depends on 10)

The apparatus as in claim 10, wherein the one or more features are real-valued.

### Claim 19

A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to:
initialize thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins;
reciprocally merge adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number;
simultaneously learn weights of a linear decision rule associated with the one or more data features while merging; and
establish a data-driven representation for a data-driven classifier based on the refined bins and learned weights.

### Claim 20 (depends on 19)

The computer-readable media as in claim 19, wherein the pre-defined bins are uniform and equidistant.