8.1 Learning Rule (Equation 70) · θ_k^{new}

Read the equation

The source describes this relation in the following context: Information is encoded holographically using golden basis functions:

8.1 Learning Rule (Equation 70)

θ_k^{new} = (1-α) × θ_k^{old} + α × w × ψ_k(h(item)) × Φ(importance)

Where:

• θ_k = k-th component of 16-dimensional knowledge vector

• α = learning rate (default 0.1)

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read θ_k^{new} as the quantity being defined or updated by this relation.

2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

Information is encoded holographically using golden basis functions:

8.1 Learning Rule (Equation 70)

θ_k^{new} = (1-α) × θ_k^{old} + α × w × ψ_k(h(item)) × Φ(importance)

Where:

• θ_k = k-th component of 16-dimensional knowledge vector

• α = learning rate (default 0.1)

θ_k^{new} = (1-α) × θ_k^{old} + α × w × ψ_k(h(item)) × Φ(importance)